Repair method of surface radiation image

Abstract

The present invention provides a method for repairing a surface radiation image, which is applied to the fields of remote sensing technology and image processing technology. The method comprises: determining, from multiple pixels of the surface radiation image to be repaired, a target similar pixel matching the pixel to be repaired, based on a repair identifier, wherein the target similar pixel is obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm; predicting a first pixel prediction value based on a dynamic adjustment parameter, the spatial distance between the pixel to be repaired and the target similar pixel, and the spectral difference; determining a time information weight of the auxiliary surface radiation image based on the spectral difference between the auxiliary pixel to be repaired and the auxiliary target similar pixel in the auxiliary surface radiation image; predicting a second pixel prediction value of the surface radiation image to be repaired at a time interval based on the time information weight; and repairing the surface radiation image to be repaired based on the first pixel prediction value and the second pixel prediction value.

Description

Technical Field

[0001] The present invention relates to the fields of remote sensing technology and image processing technology, and in particular to a method for repairing surface radiation images. Background Art

[0002] Surface radiation images (also known as surface background radiation products), as the primary data source for surface observations, have been widely used in a variety of fields, including urban construction, disaster assessment, vegetation detection, and environmental change monitoring. However, due to natural conditions or sensor system influences, surface radiation images often contain missing or distorted pixels, making it difficult to capture the true surface radiation characteristics of these pixels. To obtain the complete surface radiation characteristics of the image, surface radiation image restoration is required.

[0003] In the process of realizing the concept of the present invention, the inventors discovered that there are at least the following problems in the related art: the information used to repair the surface radiation image is relatively insufficient, resulting in low image repair accuracy. Summary of the Invention

[0004] In view of this, the present invention provides a method for repairing surface radiation images.

[0005] One aspect of the present invention provides a method for repairing a surface radiation image, comprising: determining, from a plurality of pixels of the surface radiation image to be repaired, a target similar pixel matching the pixel to be repaired, based on a repair identifier, the target pixel being obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm, and the surface radiation image to be repaired characterizes the surface radiation characteristics of the spectral band; predicting a first pixel prediction value of the pixel to be repaired relative to the target similar pixel in terms of spatial distance based on a dynamic adjustment parameter, the spatial distance and spectral difference between the pixel to be repaired and the target similar pixel, wherein the dynamic adjustment parameter is used to balance the spatial distance and spectral difference between the plurality of target similar pixels. The weight of the difference degree is used; the time information weight of the auxiliary surface radiation image is determined according to the spectral difference between the auxiliary pixel to be repaired and the auxiliary target similar pixel in the auxiliary surface radiation image, the auxiliary pixel to be repaired is determined according to the position of the pixel to be repaired in the surface radiation image to be repaired, and the auxiliary target similar pixel is determined according to the position of the target similar pixel in the surface radiation image to be repaired; according to the time information weight, the second pixel prediction value of the surface radiation image to be repaired in the time interval is predicted, and the time interval is the acquisition time difference between the surface radiation image to be repaired and the auxiliary surface radiation image; according to the first pixel prediction value and the second pixel prediction value, the surface radiation image to be repaired is repaired.

[0006] One aspect of the present invention provides a device for repairing a surface radiation image, comprising: a first determination module for determining, based on a repair identifier, a target similar pixel that matches a pixel to be repaired from multiple pixels of the surface radiation image to be repaired, wherein the target similar pixel is obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm, and the surface radiation image to be repaired characterizes the surface radiation characteristics of the spectral band; a first prediction module for predicting a first pixel prediction value of the pixel to be repaired relative to the target similar pixel in the spatial distance based on a dynamic adjustment parameter, the spatial distance between the pixel to be repaired and the target similar pixel, and the spectral difference, wherein the dynamic adjustment parameter is used to balance the multiple target similar pixels. The weight of the spatial distance and spectral difference between similar pixels; the second determination module is used to determine the time information weight of the auxiliary surface radiation image according to the spectral difference between the auxiliary pixel to be repaired and the auxiliary target similar pixel in the auxiliary surface radiation image, the auxiliary pixel to be repaired is determined according to the position of the pixel to be repaired in the surface radiation image to be repaired, and the auxiliary target similar pixel is determined according to the position of the target similar pixel in the surface radiation image to be repaired; the second prediction module is used to predict the second pixel prediction value of the surface radiation image to be repaired at the time interval according to the time information weight; the repair module is used to repair the surface radiation image to be repaired according to the first pixel prediction value and the second pixel prediction value.

[0007] Another aspect of the present invention provides an electronic device, comprising:

[0008] one or more processors;

[0009] a memory for storing one or more programs,

[0010] When one or more programs are executed by one or more processors, the one or more processors implement the above-mentioned method.

[0011] Another aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0012] Another aspect of the present invention provides a computer program product, which includes computer executable instructions. When the instructions are executed, they are used to implement the above method.

[0013] According to an embodiment of the present invention, based on the restoration identifier, a pixel to be restored is determined from multiple pixels of the surface radiation image to be restored, and a target similar pixel matching the pixel to be restored is determined from the multiple pixels, the target similar pixel being obtained by updating the offset of the similar pixel relative to the pixel to be restored based on the gradient descent algorithm; based on the dynamic adjustment parameters, the spatial distance and spectral difference between the pixel to be restored and the target similar pixel, a first pixel prediction value of the pixel to be restored relative to the target similar pixel in spatial distance is predicted; based on the spectral difference between the auxiliary pixel to be restored and the auxiliary target similar pixel in the auxiliary surface radiation image, the time information weight of the auxiliary surface radiation image is determined; based on the time information weight, a second pixel prediction value of the surface radiation image to be restored in a time interval is predicted; based on the first pixel prediction value and the second pixel prediction value, the surface radiation image to be restored is restored, that is, the spatial information of the pixel to be restored relative to the target similar pixel and the time information of the surface radiation image to be restored relative to the auxiliary surface radiation image are combined to restore the surface radiation image to be restored, so that the image restoration accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0015] Figure 1 A diagram showing an application scenario of a method for repairing a surface radiation image according to an embodiment of the present invention;

[0016] Figure 2 A flow chart of a method for repairing a surface radiation image according to an embodiment of the present invention is shown;

[0017] Figure 3 A schematic diagram showing target similar pixels in a surface radiation image to be restored according to an embodiment of the present invention is shown;

[0018] Figure 4 A schematic diagram showing a method for repairing a surface radiation image according to another embodiment of the present invention is shown;

[0019] Figure 5 A structural block diagram of a device for repairing a surface radiation image according to an embodiment of the present invention is shown;

[0020] Figure 6 A block diagram of an electronic device suitable for implementing a method for repairing a ground surface radiation image according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0022] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the presence of the features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0025] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, invention and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0026] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by embodiments of the present invention provide users with corresponding operational portals, allowing them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The term "automated decision-making" herein refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, or credit status through computer programs and making decisions. The term "expert decision-making" herein refers to the activity of decision-making by individuals who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0027] Surface radiation images are the primary data source for surface observations. However, due to natural conditions or sensor system artifacts, surface radiation images often fail to capture the true surface radiation characteristics of some pixels. To ensure complete surface radiation characteristics, surface radiation images must be restored. However, due to the limited information required to restore surface radiation images, image restoration accuracy is low.

[0028] In view of this, the present invention provides a method for repairing a surface radiation image, comprising: according to a repair identifier, selecting a target similar pixel that matches a pixel to be repaired from multiple pixels of the surface radiation image to be repaired, the target similar pixel being obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm, and the surface radiation image to be repaired characterizes the surface radiation characteristics of the spectral band; according to a dynamic adjustment parameter, the spatial distance and spectral difference between the pixel to be repaired and the target similar pixel, predicting the first pixel prediction value of the pixel to be repaired relative to the target similar pixel in the spatial distance, and the dynamic adjustment parameter being used to balance the target similar pixel. The weight of the spatial distance and spectral difference between target similar pixels is determined; the time information weight of the auxiliary surface radiation image is determined according to the spectral difference between the auxiliary pixel to be repaired and the auxiliary target similar pixel in the auxiliary surface radiation image, the auxiliary pixel to be repaired is determined according to the position of the pixel to be repaired in the surface radiation image to be repaired, and the auxiliary target similar pixel is determined according to the position of the target similar pixel in the surface radiation image to be repaired; according to the time information weight, the second pixel prediction value of the surface radiation image to be repaired in the time interval is predicted; according to the first pixel prediction value and the second pixel prediction value, the surface radiation image to be repaired is repaired.

[0029] Figure 1 A diagram showing an application scenario of a method for repairing a surface radiation image according to an embodiment of the present invention is shown.

[0030] like Figure 1As shown, the application scenario according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables, etc.

[0031] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0032] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. For example, the first terminal device 101, the second terminal device 102, and the third terminal device 103 can be used to store surface radiation images.

[0033] The server 105 may be a server that provides various services. For example, when a user sends a request to the server 105 to repair a locally stored surface radiation image using the first terminal device 101, the second terminal device 102, or the third terminal device 103, the backend management server may repair the surface radiation image in response to the received user request and feed the repaired surface radiation image back to the terminal device.

[0034] It should be noted that the surface radiation image repair method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the surface radiation image repair device provided in the embodiment of the present invention can generally be set in the server 105. The surface radiation image repair method provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the surface radiation image repair device provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0035] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0036] Figure 2 A flow chart of a method for repairing a surface radiation image according to an embodiment of the present invention is shown.

[0037] like Figure 2 As shown, the surface radiation image repair method of this embodiment includes operations S210 to S250.

[0038] In operation S210 , a target similar pixel matching the pixel to be restored is determined from a plurality of pixels of the surface radiation image to be restored according to the restoration identifier.

[0039] According to an embodiment of the present invention, the repair identifier may be a unique identifier of an invalid pixel value, for example, a symbol such as "-1", "X", or "589".

[0040] According to an embodiment of the present invention, the surface radiation image to be restored represents the surface radiation characteristics of the spectral band. For example, the surface radiation image to be restored can be a remote sensing image of the surface captured by a sensor on an aerospace platform. The surface radiation image to be restored can also be a reflectivity map of the surface captured by a sensor on an aerospace platform.

[0041] According to an embodiment of the present invention, the target similar pixel is obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm. The similar pixel has similarity with the pixel to be repaired.

[0042] According to an embodiment of the present invention, the target similar pixel represents the same type of ground object as that represented by the pixel to be restored. For example, if the pixel to be restored represents a plant leaf, the target similar pixel also represents a plant leaf.

[0043] In operation S220, a first pixel prediction value of the pixel to be repaired relative to the target similar pixel in terms of spatial distance is predicted based on the dynamic adjustment parameter, the spatial distance between the pixel to be repaired and the target similar pixel, and the spectral difference.

[0044] The dynamic adjustment parameter is used to balance the weight of the spatial distance and spectral difference between the target similar pixels, which can improve the accuracy of the first pixel prediction value.

[0045] According to an embodiment of the present invention, the spatial distance between the pixel to be restored and the target similar pixel is mainly determined by the coordinates and geometric positions of the pixel to be restored and the target similar pixel.

[0046] For example, the surface radiation image to be restored may also be a reflectance map of the surface taken by a sensor of an aerospace platform, and the spectral difference may be obtained by calculating the Euclidean distance between the reflectances of the pixel to be restored and the target similar pixel.

[0047] For example, the surface radiation image to be restored could be a remote sensing image of the surface captured by a sensor on an aerospace platform. Based on the mapping relationship between reflectance and pixel values, the reflectance of the pixel to be restored and the target pixel with similar characteristics can be obtained. The Euclidean distance between the reflectances of the pixel to be restored and the target pixel with similar characteristics can then be calculated to determine the spectral difference.

[0048] For example, the surface radiation image to be restored can be a remote sensing image of the surface captured by a sensor on an aerospace platform. The spectral vectors of the pixel to be restored and the target similar pixel are obtained from the surface radiation image to be restored. The angle between the spectral vector of the pixel to be restored and the spectral vector of the target similar pixel is calculated to obtain the spectral difference.

[0049] In operation S230 , a time information weight of the auxiliary surface radiation image is determined according to a spectral difference between the auxiliary pixel to be restored and the auxiliary target similar pixel in the auxiliary surface radiation image.

[0050] According to an embodiment of the present invention, the auxiliary surface radiation image is obtained based on the image identifier of the surface radiation image to be restored. The image identifier can be a geographic location. For example, the image identifier can be (longitude, latitude).

[0051] For example, the auxiliary surface radiation image and the surface radiation image to be restored have the same shooting content, but are shot at different times.

[0052] According to an embodiment of the present invention, the auxiliary pixel to be restored is determined from a plurality of auxiliary pixels in the auxiliary surface radiation image according to the position of the pixel to be restored in the surface radiation image to be restored.

[0053] For example, the position of the auxiliary pixel to be restored in the auxiliary surface radiation image is the same as the position of the pixel to be restored in the surface radiation image to be restored.

[0054] According to an embodiment of the present invention, the auxiliary target similar pixel is determined from a plurality of auxiliary pixels according to the position of the target similar pixel in the surface radiation image to be restored.

[0055] For example, the position of the auxiliary target similar pixel in the auxiliary surface radiation image is the same as the position of the target similar pixel in the surface radiation image to be restored.

[0056] According to an embodiment of the present invention, the temporal information weight represents the degree of change in the surface spectral characteristics during the time interval between capturing the auxiliary surface radiation image and capturing the surface radiation image to be restored. For example, the greater the temporal information weight, the smaller the change in the surface spectral characteristics.

[0057] In operation S240, a second pixel prediction value of the surface radiation image to be restored at a time interval is predicted based on the time information weight.

[0058] According to an embodiment of the present invention, the time interval is the acquisition time difference between the surface radiation image to be restored and the auxiliary surface radiation image.

[0059] In operation S250, the surface radiation image to be restored is restored according to the first pixel prediction value and the second pixel prediction value.

[0060] According to an embodiment of the present invention, the effects of the first pixel prediction value and the second pixel prediction value on the change in spectral characteristics can be combined to calculate the target pixel prediction value of the pixel to be restored. The pixel to be restored in the surface radiation image to be restored is filled with a color corresponding to the target pixel prediction value.

[0061] According to an embodiment of the present invention, based on the restoration identifier, a pixel to be restored is determined from multiple pixels of the surface radiation image to be restored, and a target similar pixel matching the pixel to be restored is determined from the multiple pixels; based on the dynamic adjustment parameters, the spatial distance and spectral difference between the pixel to be restored and the target similar pixel, a first pixel prediction value of the pixel to be restored relative to the target similar pixel in terms of spatial distance is predicted; based on the spectral difference between the auxiliary pixel to be restored and the auxiliary target similar pixel in the auxiliary surface radiation image, a time information weight of the auxiliary surface radiation image is determined; based on the time information weight, a second pixel prediction value of the surface radiation image to be restored in terms of time interval is predicted; based on the first pixel prediction value and the second pixel prediction value, the surface radiation image to be restored is restored, that is, the surface radiation image to be restored is restored by combining the spatial information of the pixel to be restored relative to the target similar pixel and the time information of the surface radiation image to be restored relative to the auxiliary surface radiation image, so that the image restoration accuracy is improved.

[0062] According to an embodiment of the present invention, the above method further includes: performing cluster analysis on multiple pixels in the surface radiation image to be restored to obtain target similar pixels.

[0063] For example, based on the restoration identifier, the surface radiation image to be restored is searched for the pixels to be restored, and cluster analysis is performed on multiple pixels in the surface radiation image to be restored to obtain target similar pixels to the pixels to be restored.

[0064] Figure 3A schematic diagram of target similar pixels in a surface radiation image to be restored according to an embodiment of the present invention is shown.

[0065] like Figure 3 As shown, the surface radiation image to be repaired has an area to be repaired and two centers of the area to be repaired with different image identifiers. Image identifier " " indicates the center of the area to be repaired 1, image identification " ” represents the center of the area to be restored 2. Similar pixels are distributed in multiple locations of the surface radiation image to be restored.

[0066] For example, the spatial distance between the pixel to be repaired and the target similar pixel is calculated as shown in formula (1), and the spectral difference between the pixel to be repaired and the target similar pixel is calculated as shown in formula (3).

[0067] For the surface radiation image to be repaired, which includes multiple target similar pixels, since the spatial distance between the pixel to be repaired and the target similar pixel may be very different, while the spectral distance is small, it may make the spatial distance range difficult to compare with the spectral distance. Therefore, the spatial distance and spectral distance are normalized according to formula (3) and formula (4).

[0068] (1);

[0069] i represents the i-th target similar pixel, i is an integer greater than 1, the pixel to be repaired is located at (x, y) of the image to be repaired, and the target similar pixel is located at (x i ,y i ), Di is the spatial distance between the pixel to be repaired and the target similar pixel. p is the distance metric parameter. When p=1, it is equivalent to the Manhattan distance, which emphasizes the sum of the absolute differences in each dimension. When p=2, it is equivalent to the Euclidean distance, which tends to consider the sum of the squares of the differences in each dimension. You can choose the appropriate calculation method as needed.

[0070] (2);

[0071] D min and D max are the minimum and maximum spatial distances among all target similar pixels, respectively. ε is the correction factor. is the normalized spatial distance.

[0072] (3);

[0073] t n The pixel value of the auxiliary pixel to be repaired at (x, y) in the middle b band of the auxiliary surface radiation image at the time, t n The middle b band at the historical moment is located at the auxiliary surface radiation image (x i ,y i ), K is the number of bands, is the spectral difference between the pixel to be repaired and the target similar pixel.

[0074] (4);

[0075] is the normalized spectral difference, is the minimum value of the spectral difference of all target similar pixels, is the maximum value of the spectral difference of all target similar pixels. ε is the correction factor.

[0076] The above method also includes: calculating a dynamic adjustment parameter based on the normalized spatial distance, the normalized spectral difference and the adjustment coefficient.

[0077] According to an embodiment of the present invention, the formula for spatial information weight is as follows:

[0078] (5);

[0079] (6);

[0080] i represents the i-th target similar pixel, and is the normalized spatial distance and the normalized spectral difference, W i is the spatial information weight of the i-th target similar pixel. i It is a dynamic adjustment parameter used to balance the weight of spatial distance and spectral difference between multiple similar pixels, so as to increase the weight of spectral similarity when the spectral similarity is high; α0 is the basic weight value used to initialize α i , for example, α0 is 1; k is the adjustment coefficient used to control α i The range of change, k can be 0.5.

[0081] For example, if the number of target similar pixels is 30, the spatial information weights of the 30 pixels can be compared to obtain the maximum spatial information weight, which is then used to predict the first pixel prediction value and the second pixel prediction value.

[0082] The offset of each similar pixel relative to the position of the pixel to be repaired is counted to obtain the initial offset set, as shown in formula (7):

[0083] (7);

[0084] in, It represents the offset of the ith similar pixel corresponding to the pixel to be repaired, that is, . Similar pixels and target similar pixels are in one-to-one correspondence.

[0085] Considering the different degrees of influence of different similar pixels on the pixels to be restored, a weighted evaluation is performed from two dimensions: spatial spectral characteristics and temporal spectral characteristics. Assuming that the land cover type does not change in a short period of time, the auxiliary surface radiation image is used to construct the MRF (Markov Random Field) energy function according to Equation (8) for calculation.

[0086] (8);

[0087] in, represents the missing area, (x, y) represents the position of the pixel to be repaired in the image to be repaired; represents the auxiliary pixel to be repaired at the (x, y) position in the auxiliary surface radiation image; N represents the number of pixels in the spatially connected area of the auxiliary surface radiation image; Represents the adjacent pixels in the spatially connected region of the pixel to be repaired in the image to be repaired; Indicates the offset between the position of the pixel to be repaired and the position of the similar pixel; Indicates the offset between the position of the auxiliary pixel to be repaired at the (x, y) position in the auxiliary surface radiation image and the position of the auxiliary similar pixel; Represents the data item, ensuring that the pixels used are in the valid area of the image. If the similar pixel is in the valid area of the image to be repaired, it is 0, otherwise +1; represents the time smoothing term, and the specific calculation formula is as shown in formula (8); represents the spatial smoothing term, and the specific calculation formula is as shown in formula (9); α and β are used to balance the temporal smoothing term respectively. and spatial smoothing terms , set them to 1 and 0.5.

[0088] (9);

[0089] (10);

[0090] in, Represents the offset of the i-th similar pixel relative to the pixel to be repaired; The auxiliary surface radiation image The offset of the auxiliary similar pixel relative to the auxiliary pixel to be repaired at the (x, y) position in the auxiliary surface radiation image; represents the adjacent pixels in the spatially connected region of the i-th similar pixel in the auxiliary surface radiation image; Represents the pixel value of the pixel to be repaired at (x, y); m represents the total number of similar pixels, and the number of similar pixels can be the same as the number of target similar pixels. and They all represent the pixel values of adjacent pixels in the spatially connected area of the i-th similar pixel in the auxiliary surface radiation image.

[0091] Use the gradient descent algorithm to update the offset of each similar pixel relative to the pixel to be repaired :

[0092] (11);

[0093] in, is the total energy function in The gradient at is the learning rate, which can be set to 0.001.

[0094] According to an embodiment of the present invention, based on the dynamic adjustment parameters, the spatial distance and spectral difference between the pixel to be repaired and the target similar pixel, the first pixel prediction value of the pixel to be repaired relative to the target similar pixel in the spatial distance is predicted, including: determining the spatial information weight of the target similar pixel based on the dynamic adjustment parameters, the spatial distance and spectral difference between the pixel to be repaired and the target similar pixel; based on the pixel value of the auxiliary target similar pixel and the spatial information weight of the target similar pixel, predicting the first pixel prediction value of the pixel to be repaired relative to the target similar pixel in the spatial distance.

[0095] According to an embodiment of the present invention, the weighted average pixel value of all auxiliary target similar pixels can be calculated based on the pixel values of the auxiliary target similar pixels and the spatial information weights of the target similar pixels; and then the first pixel prediction value is predicted based on the weighted average pixel value.

[0096] According to an embodiment of the present invention, the surface radiation image to be repaired includes multiple target-similar pixels; based on the pixel values of the auxiliary target-similar pixels and the spatial information weights of the target-similar pixels, predicting the first pixel prediction value of the pixel to be repaired relative to the target-similar pixels in spatial distance includes: obtaining the first pixel prediction value to be counted of the auxiliary surface radiation image based on the pixel values of each of the multiple auxiliary target-similar pixels and the spatial information weights of each of the multiple target-similar pixels; for the auxiliary surface radiation images at multiple moments, predicting the first pixel prediction value based on the corresponding first pixel prediction values to be counted of each of the auxiliary surface radiation images at multiple moments.

[0097] According to an embodiment of the present invention, the surface radiation image to be restored includes a plurality of target-similar pixels, that is, the auxiliary restoration surface radiation image also includes a plurality of auxiliary target-similar pixels.

[0098] According to an embodiment of the present invention, the average value of the first pixel prediction values to be counted of the auxiliary surface radiation image at multiple moments can be calculated, that is, the average value of the first pixel prediction values to be counted of the auxiliary surface radiation image at multiple moments is the first pixel prediction value to be counted, and then the first pixel prediction value is predicted based on the average value of the first pixel prediction values to be counted.

[0099] Since the coordinate offset is updated within the spatial connectivity of the preliminarily determined similar pixels, the spatial distance and spectral difference do not change much. Therefore, the spectral spatial information weight corresponding to each similar pixel obtained preliminarily is continued to calculate and obtain the predicted value based on the spectral spatial information for each auxiliary phase.

[0100] For example, considering the different influences of different target similar pixels on the pixels to be repaired, weighted evaluation is performed from two dimensions: spatial spectral characteristics and temporal spectral characteristics. When the land cover type does not change in a short period of time, the four phase images of adjacent phases and the same period of the previous and next two years are respectively used to calculate the first pixel prediction value to be counted according to formula (12). Calculation. Based on the average of the first pixel prediction values to be counted of the auxiliary surface radiation images at four different times (as shown in Formula 13), a more robust and comprehensive first pixel prediction value of the spectral spatial distance is obtained. .

[0101] (12);

[0102] (13);

[0103] T is the number of auxiliary surface radiation images. For example, if T is 4, there are four auxiliary surface radiation images at different times.

[0104] When calculating pixel prediction values based on spectral time intervals, the time information weight W is added tn In order to improve the temporal and spatial consistency of the algorithm restoration results, the weight of each auxiliary surface radiation image for calculating the second pixel prediction value at the time interval is determined according to the minimum spectral difference calculated by each auxiliary surface radiation image. Then, the spatial information weight W is comprehensively used i and time information weight W tn , to obtain more accurate pixel prediction values.

[0105] According to an embodiment of the present invention, determining the time information weight of the auxiliary surface radiation image based on the spatial distance between the auxiliary pixel to be repaired and the auxiliary target similar pixel in the auxiliary surface radiation image includes: for the auxiliary surface radiation image including multiple auxiliary target similar pixels, comparing multiple spectral differences corresponding to the multiple auxiliary target similar pixels to obtain the target spectral difference; determining the time information weight of the auxiliary surface radiation image based on the target spectral difference.

[0106] According to an embodiment of the present invention, the target spectral difference may be the minimum spectral difference among a plurality of spectral differences corresponding to a plurality of auxiliary target similar pixels.

[0107] According to an embodiment of the present invention, determining the time information weight of the auxiliary surface radiation image according to the target spectral difference includes:

[0108] Determining a comprehensive spectral difference reference value of the auxiliary surface radiation images at multiple moments according to the target spectral difference corresponding to each of the auxiliary surface radiation images at multiple moments;

[0109] Determine the spectral difference reference value according to the target spectral difference of the auxiliary surface radiation image;

[0110] The time information weight is determined according to the spectral difference reference value and the comprehensive spectral difference reference value.

[0111] According to an embodiment of the present invention, the formula for the time information weight is as follows:

[0112] (14);

[0113] n represents the nth auxiliary surface radiation image, T is the number of auxiliary surface radiation images, For t n The target spectral difference corresponding to the auxiliary surface radiation image at the time is the minimum value of the spectral difference of all similar pixels of the auxiliary target; W tn t n The time information weight of the auxiliary surface radiation image at time . is the reference value of comprehensive spectral difference, is the reference value of spectral difference.

[0114] According to an embodiment of the present invention, based on the time information weight, predicting the second pixel prediction value of the surface radiation image to be repaired at a time interval includes: for the surface radiation image to be repaired, including multiple target similar pixels, according to the respective time information weights of the auxiliary surface radiation images at multiple moments and the respective spatial information weights of the multiple target similar pixels, calculating the time repair pixel value of the surface radiation image to be repaired; based on the time repair pixel value, predicting the pixel value of the pixel to be repaired at a time interval to obtain the second pixel prediction value.

[0115] According to an embodiment of the present invention, the temporal restoration pixel value The calculation formula is as follows:

[0116] = (15);

[0117] The method includes: predicting the pixel value of the auxiliary pixel to be repaired at a time interval based on the time repair pixel value to obtain a second pixel prediction value, including: determining the pixel value of the auxiliary pixel to be repaired; and determining the second pixel prediction value according to the pixel value of the auxiliary pixel to be repaired and the time repair pixel value.

[0118] According to an embodiment of the present invention, the calculation formula of the second pixel prediction value is as follows:

[0119] (16);

[0120] N is the number of target similar pixels, T is the number of auxiliary surface radiation images, The b band is located at the image to be repaired at time t (x+sx i ,y+sy i ) is the pixel value of the pixel to be repaired at, t n The middle b band at the moment is located at (x+sx i ,y+sy i ) is the pixel value of the auxiliary pixel to be repaired at, t n The pixel value of the auxiliary pixel to be repaired at (x, y) in the middle b band of the auxiliary surface radiation image at the time, is the predicted value of the second pixel.

[0121] According to an embodiment of the present invention, repairing the surface radiation image to be repaired based on the first pixel prediction value and the second pixel prediction value includes: repairing the surface radiation image to be repaired based on the statistical spatial distance between multiple target similar pixels and the pixel to be repaired, the spatial distance between the pixel to be repaired and the center of the area to be repaired, the first pixel prediction value and the second pixel prediction value, and the statistical spatial distance is determined based on the spatial distances corresponding to the multiple target similar pixels.

[0122] According to an embodiment of the present invention, the statistical spatial distance may be an average spatial distance of spatial distances corresponding to a plurality of target similar pixels.

[0123] According to an embodiment of the present invention, a target pixel prediction value is obtained based on the statistical spatial distance between multiple target similar pixels and the pixel to be repaired, the spatial distance between the pixel to be repaired and the center of the area to be repaired, the first pixel prediction value and the second pixel prediction value. The target pixel prediction value is used to repair the pixel to be repaired in the surface radiation image to be repaired.

[0124] The calculation formula of the target pixel prediction value is as follows:

[0125] (17);

[0126] r1 is the average spatial distance between the pixel to be repaired and the target similar pixel, r2 is the spatial distance between the pixel to be repaired and the center of the area to be repaired. When the pixel to be repaired is close to the boundary of the area to be repaired, the first pixel prediction value is The prediction value based on the spectral spatial distance is more reliable, so the weight is greater and the spatial continuity can be better guaranteed. If the pixel to be repaired is near the center of the area to be repaired, the second pixel prediction value based on the spectral time interval is More reliable. Dynamically adjust the prediction weight based on the distance of the pixel to be repaired relative to the center or boundary of the area to be repaired.

[0127] For example, leveraging the characteristic that the spectrum of ground objects remains constant over a short period of time, adjacent time phases of the surface radiation image to be restored and images from the same period two years before and after are combined to form auxiliary surface radiation images (a total of four auxiliary surface radiation images at each time point). Typically, a uniform value is assigned to the affected anomalous pixels according to the data product user manual. This assigned value serves as a restoration identifier. Based on the restoration identifier, similar target pixels are searched within the surface radiation image to be restored and weighted to determine the spatial information weight of each similar target pixel. An image identifier for the surface radiation image to be restored is generated based on the geographic location of the target area. Based on the image identifier, auxiliary surface radiation images corresponding to the target area at different times are obtained. Based on the pixel values of the auxiliary target-similar pixels in the auxiliary surface radiation image and their spatial information weights, a weighted average pixel value of all auxiliary target-similar pixels is calculated. This weighted average pixel value is then used to predict a more stable first pixel prediction value at a spectral spatial distance.

[0128] On the basis of spatial information weight, time information weight is further introduced. For the surface radiation image to be repaired, which includes multiple target similar pixels, the time repair pixel value of the surface radiation image to be repaired is calculated according to the time information weight of each auxiliary surface radiation image at multiple moments and the spatial information weight of each multiple target similar pixels; based on the time repair pixel value, the pixel value of the pixel to be repaired is predicted at the time interval to obtain the second pixel prediction value. Finally, the repair of the pixel to be repaired is achieved by combining the first pixel prediction value at the spectral spatial distance and the second pixel prediction value at the spectral time interval. The image data obtained by the same sensor is fully utilized to realize the high-precision repair of surface background radiation data, effectively reducing the influence of noise and outliers, making subsequent data applications more accurate and reliable, and having important practical application value.

[0129] Figure 4 A schematic diagram of a method for repairing a surface radiation image according to another embodiment of the present invention is shown.

[0130] like Figure 4As shown, cluster analysis is performed on multiple pixels 401 in the surface radiation image to be repaired to obtain similar pixels to the pixel to be repaired. The target similar pixel 402 is obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on the gradient descent algorithm. According to the spatial distance and spectral difference of the target similar pixel 402, the spatial information weight 403 of the pixel to be repaired relative to the target similar pixel in the spatial distance is calculated by formula (5). According to the spatial information weight 403 and the minimum spectral difference in the auxiliary surface radiation images at different times, the time information weight 404 of the surface radiation image to be repaired in the time interval is calculated by formula (14). Combining the time information weight 404 and the spatial information weight 403, the second pixel prediction value 405 of the surface radiation image to be repaired in the time interval is calculated by formula (16).

[0131] Based on the position of the target similar pixel 402 in the surface radiation image to be restored, an auxiliary target similar pixel 407 is determined from multiple auxiliary pixels 406 in the auxiliary surface radiation images at multiple different times. The first pixel prediction value 408 to be counted in the spatial distance of the auxiliary surface radiation images at multiple different times is calculated using formula (12). The first pixel prediction value 408 to be counted of the auxiliary surface radiation images at multiple different times is then averaged (as shown in formula (13)) to obtain the first pixel prediction value 409 in the spatial distance of the surface radiation image to be restored. Based on the statistical spatial distances between the multiple target similar pixels and the pixel to be restored, the spatial distance between the pixel to be restored and the center of the area to be restored, the first pixel prediction value 409, and the second pixel prediction value 405, the target pixel prediction value 410 is obtained using formula (17).

[0132] By comprehensively utilizing imagery from adjacent time periods and the same period two years before and after, and taking advantage of the fact that the spectral characteristics of ground objects remain largely unchanged over time, we effectively repaired the surface background radiation data. This method more accurately fills missing values, reduces data gaps caused by cloud cover, sensor failures, and other factors, and thus improves the overall data quality.

[0133] The pixels to be restored are labeled uniformly using the descriptions of the pixels in the data product user manual, eliminating uncertainty in the location of the restored pixels. During the restoration process, the most representative similar pixels are selected, and each target similar pixel is assigned an appropriate weight based on spatial, temporal, and spectral information. This reduces the impact of noise and provides more accurate surface background radiation data.

[0134] This approach does not rely on remote sensing data from other sensors. Compared to methods that use remote sensing data from other sensors as auxiliary data for inpainting, it does not require preprocessing steps such as image registration and image fusion. Remote sensing image inpainting is not limited to a specific ground feature or underlying surface and can be used on complex surfaces, with a wide range of applications.

[0135] Figure 5 A structural block diagram of a device for repairing a surface radiation image according to an embodiment of the present invention is shown.

[0136] like Figure 5 As shown, the surface radiation image restoration device of this embodiment includes a first determination module 510 , a first prediction module 520 , a second determination module 530 , a second prediction module 540 and a restoration module 550 .

[0137] First determination module 510 is used to determine, based on the restoration identifier, a target similar pixel that matches the pixel to be restored from multiple pixels in the surface radiation image to be restored. The target similar pixel is obtained by updating the offset of the similar pixel relative to the pixel to be restored using a gradient descent algorithm. The surface radiation image to be restored represents the surface radiation characteristics of the spectral band. In one embodiment, first determination module 510 can be used to perform operation S210 described above, which will not be repeated here.

[0138] The first prediction module 520 is configured to predict a first pixel prediction value of the spatial distance of the pixel to be restored relative to the target similar pixel based on the dynamic adjustment parameter, the spatial distance between the pixel to be restored, and the spectral difference between the target similar pixel. The dynamic adjustment parameter is used to balance the weights of the spatial distance and spectral difference between the multiple target similar pixels. In one embodiment, the first prediction module 520 can be configured to perform operation S220 described above and will not be further described here.

[0139] The second determination module 530 is configured to determine the temporal information weight of the auxiliary surface radiation image based on the spectral difference between the auxiliary pixel to be restored and the auxiliary target-similar pixel in the auxiliary surface radiation image. The auxiliary pixel to be restored is determined based on the position of the pixel to be restored in the surface radiation image to be restored, and the auxiliary target-similar pixel is determined based on the position of the target-similar pixel in the surface radiation image to be restored. In one embodiment, the second determination module 530 can be configured to perform operation S230 described above, which will not be further described here.

[0140] The second prediction module 540 is used to predict the second pixel prediction value of the surface radiation image to be restored at the time interval according to the time information weight. In one embodiment, the second prediction module 540 can be used to perform the operation S240 described above, which will not be repeated here.

[0141] The restoration module 550 is configured to restore the surface radiation image to be restored based on the first pixel prediction value and the second pixel prediction value. In one embodiment, the restoration module 550 may be configured to perform the operation S250 described above, which will not be described in detail herein.

[0142] According to an embodiment of the present invention, the first prediction module 520 includes a first determination submodule and a first prediction submodule. The first determination submodule is configured to determine the spatial information weight of the target similar pixel based on the spatial distance and spectral difference between the pixel to be restored and the target similar pixel; and the first prediction submodule is configured to predict a first pixel prediction value of the spatial distance between the pixel to be restored and the target similar pixel based on the pixel value of the auxiliary target similar pixel and the spatial information weight of the target similar pixel.

[0143] According to an embodiment of the present invention, the first prediction submodule includes a first obtaining unit and a first prediction unit. The first obtaining unit is used to obtain a first pixel prediction value to be counted of the surface radiation image to be restored based on the pixel values of each of the multiple auxiliary target similar pixels and the spatial information weights of each of the multiple target similar pixels, for a surface radiation image to be restored including multiple target similar pixels; the first prediction unit is used to predict a first pixel prediction value based on the corresponding first pixel prediction values to be counted of the auxiliary surface radiation images at multiple moments.

[0144] According to an embodiment of the present invention, the second prediction module 540 includes a calculation submodule and a second prediction submodule. The calculation submodule is configured to calculate, for a plurality of target similar pixels in the surface radiation image to be restored, a temporal restoration pixel value of the surface radiation image to be restored based on the temporal information weights of the auxiliary surface radiation images at a plurality of moments and the spatial information weights of the plurality of target similar pixels; and the second prediction submodule is configured to predict the pixel value of the pixel to be restored at a time interval based on the temporal restoration pixel value to obtain a second pixel prediction value.

[0145] According to an embodiment of the present invention, the restoration module 550 includes a restoration submodule. The restoration submodule is configured to restore the surface radiation image to be restored based on the statistical spatial distances between a plurality of target similar pixels and the pixel to be restored, the spatial distance between the pixel to be restored and the center of the region to be restored, the first pixel prediction value, and the second pixel prediction value. The statistical spatial distance is determined based on the spatial distances corresponding to the plurality of target similar pixels.

[0146] According to an embodiment of the present invention, the second determination module 530 includes a comparison submodule and a second determination submodule. The comparison submodule is configured to compare a plurality of spectral differences corresponding to a plurality of auxiliary target-similar pixels in the auxiliary surface radiation image to obtain a target spectral difference; and the second determination submodule is configured to determine a time information weight of the auxiliary surface radiation image based on the target spectral difference.

[0147] According to an embodiment of the present invention, the apparatus further comprises a cluster analysis module, which is configured to perform cluster analysis on a plurality of pixels in the surface radiation image to be restored, and obtain target similar pixels.

[0148] According to an embodiment of the present invention, the formula for the time information weight is shown in formula (14).

[0149] According to an embodiment of the present invention, the formula of the spatial information weight is shown in formula (5).

[0150] According to an embodiment of the present invention, the calculation formula for the second pixel prediction value is shown in formula (16).

[0151] According to embodiments of the present invention, any multiple modules among the first determination module 510, the first prediction module 520, the second determination module 530, the second prediction module 540, and the repair module 550 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present invention, at least one of the first determination module 510, the first prediction module 520, the second determination module 530, the second prediction module 540, and the repair module 550 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or implemented in any one of software, hardware, and firmware, or any suitable combination of these. Alternatively, at least one of the first determination module 510, the first prediction module 520, the second determination module 530, the second prediction module 540 and the repair module 550 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0152] Figure 6 A block diagram of an electronic device suitable for implementing a method for repairing a ground surface radiation image according to an embodiment of the present invention is shown.

[0153] like Figure 6As shown, an electronic device according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 602 or programs loaded from a storage unit 608 into a random access memory (RAM) 603. Processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or related chipsets and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0154] Various programs and data required for the operation of the electronic device are stored in RAM 603. Processor 601, ROM 602, and RAM 603 are connected to each other via bus 604. Processor 601 executes the programs in ROM 602 and / or RAM 603 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0155] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage portion 608 including a hard disk; and a communication portion 609 including a network interface card such as a LAN card or a modem. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read from the removable media can be installed in the storage portion 608 as needed.

[0156] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0157] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0158] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code causes the computer system to implement the surface radiation image restoration method provided by the embodiment of the present invention.

[0159] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when the computer program is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0160] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0161] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609 and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0162] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0163] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0164] It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or couplings fall within the scope of the present invention.

[0165] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for repairing a surface radiation image, characterized in that: include: Determining, from multiple pixels of the surface radiation image to be repaired, a target similar pixel that matches the pixel to be repaired, based on the repair identifier, wherein the target similar pixel is obtained by updating the offset of the similar pixel relative to the pixel to be repaired based on a gradient descent algorithm, the target similar pixel represents the same type of ground object as the pixel to be repaired, and the surface radiation image to be repaired represents surface radiation characteristics of a spectral band; Determining the spatial information weight of the target similar pixel according to the dynamic adjustment parameter, the spatial distance and the spectral difference between the pixel to be repaired and the target similar pixel, wherein the dynamic adjustment parameter is used to balance the weights of the spatial distance and the spectral difference between the plurality of target similar pixels; Predicting a first pixel prediction value of the pixel to be restored relative to the target similar pixel at the spatial distance based on the pixel value of the auxiliary target similar pixel in the auxiliary surface radiation image and the spatial information weight of the target similar pixel, wherein the auxiliary surface radiation image and the surface radiation image to be restored have the same captured content, and the auxiliary target similar pixel is determined based on the position of the target similar pixel in the surface radiation image to be restored; determining a time information weight of the auxiliary surface radiation image based on a spectral difference between an auxiliary pixel to be restored and the auxiliary target similar pixel in the auxiliary surface radiation image, wherein the auxiliary pixel to be restored is determined based on a position of the pixel to be restored in the surface radiation image to be restored; Predicting a second pixel prediction value of the surface radiation image to be restored at a time interval according to the time information weight; The surface radiation image to be repaired is repaired according to the first pixel prediction value and the second pixel prediction value.

2. The method according to claim 1, characterized in that The surface radiation image to be restored includes a plurality of target similar pixels; and the step of predicting the first pixel prediction value of the pixel to be restored relative to the target similar pixel at the spatial distance based on the pixel value of the auxiliary target similar pixel and the spatial information weight of the target similar pixel includes: Obtaining a first pixel prediction value to be counted of the auxiliary surface radiation image according to the pixel values of each of the plurality of auxiliary target similar pixels and the spatial information weights of each of the plurality of target similar pixels; For the auxiliary surface radiation images at multiple moments, the first pixel prediction value is predicted based on the first pixel prediction value to be counted corresponding to each of the auxiliary surface radiation images at multiple moments.

3. The method according to claim 2, characterized in that The predicting, based on the time information weight, to obtain a second pixel prediction value of the surface radiation image to be restored at a time interval includes: For the surface radiation image to be restored including a plurality of target similar pixels, the time restoration pixel value of the surface radiation image to be restored is calculated based on the time information weights of the auxiliary surface radiation images at a plurality of moments and the spatial information weights of the plurality of target similar pixels; The pixel value of the pixel to be repaired is predicted at the time interval based on the time repair pixel value to obtain the second pixel prediction value.

4. The method according to claim 3, characterized in that The step of predicting the pixel value of the pixel to be repaired at the time interval based on the temporal repair pixel value to obtain the second pixel prediction value includes: Determining the pixel value of the auxiliary pixel to be repaired; The second pixel prediction value is determined according to the pixel value of the auxiliary pixel to be repaired and the temporal repair pixel value.

5. The method according to claim 2, characterized in that The repairing of the surface radiation image to be repaired according to the first pixel prediction value and the second pixel prediction value includes: The surface radiation image to be repaired is repaired based on the statistical spatial distances between multiple target similar pixels and the pixels to be repaired, the spatial distance between the pixels to be repaired and the center of the area to be repaired, the first pixel prediction value and the second pixel prediction value. The statistical spatial distance is determined based on the spatial distances corresponding to the multiple target similar pixels respectively.

6. The method according to claim 1, characterized in that The determining of the time information weight of the auxiliary surface radiation image according to the spectral difference between the auxiliary pixel to be restored and the auxiliary target similar pixel in the auxiliary surface radiation image comprises: The auxiliary surface radiation image includes a plurality of auxiliary target similar pixels, comparing a plurality of spectral differences corresponding to the plurality of auxiliary target similar pixels to obtain a target spectral difference; The time information weight of the auxiliary surface radiation image is determined according to the target spectral difference.

7. The method according to claim 6, characterized in that The determining the time information weight of the auxiliary surface radiation image according to the target spectral difference includes: determining comprehensive spectral difference reference values of the auxiliary surface radiation images at multiple moments according to the target spectral differences corresponding to the auxiliary surface radiation images at multiple moments; determining a spectral difference reference value according to the target spectral difference of the auxiliary surface radiation image; The time information weight is determined according to the spectrum difference reference value and the comprehensive spectrum difference reference value.

8. The method according to claim 1, characterized in that The method further comprises: Cluster analysis is performed on the plurality of pixels in the surface radiation image to be restored to obtain the target similar pixels.

9. The method according to claim 1, characterized in that The method further comprises: The dynamic adjustment parameter is calculated based on the normalized spatial distance, the normalized spectral difference and the adjustment coefficient.

Images