Hot spot identification method, device and equipment based on remote sensing image

Abstract

The present invention relates to the field of data analysis, and in particular to a hotspot identification method based on remote sensing images, comprising: obtaining a remote sensing image data set of a target area, obtaining a time series of the remote sensing image data set according to time points of remote sensing images in the remote sensing image data set; decomposing the time series according to a set empirical mode decomposition algorithm to obtain an IMF subsequence of the remote sensing image data set; obtaining a hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and a hotspot index calculation algorithm; obtaining a spatial weight parameter of the remote sensing image, and obtaining a hotspot area of ​​the target area according to the hotspot index and the spatial weight parameter; responding to a hotspot area display instruction, the hotspot area display instruction including a remote sensing image data set of a to-be-identified area, obtaining electronic map data, obtaining a hotspot area corresponding to the remote sensing image data set of the to-be-identified area according to the remote sensing image data set of the to-be-identified area, and displaying and marking the hotspot area on the electronic map data.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a hotspot identification method, device, equipment and storage medium based on remote sensing images. Background Art

[0002] Remote sensing satellites have the advantage of long-term observation and have accumulated a vast amount of historical archived data over the past few decades. This allows users to identify hotspots through these remote sensing images, explore the temporal and spatial changes of the surface, and reveal the spatial differentiation of geographical phenomena caused by long-term evolution.

[0003] Since the trend characteristics and periodic characteristics of geographical phenomena often coexist, whether the trend characteristics of different regions have significant differences depends not only on the size of the rate of change, but also on the strength of the periodic characteristics. The current technical solution uses linear regression or similar algorithms to calculate the linear change rate of remote sensing images to characterize the change trend of the observed object, which is not accurate enough. Therefore, it is of great significance to propose an accurate hotspot identification method based on remote sensing images. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a hotspot identification method, device, equipment and storage medium based on remote sensing images, which uses a set empirical mode decomposition algorithm to separate trend signals and periodic signals with nonlinear characteristics, construct a hotspot index according to the amount of information of the two, identify hotspots, fully consider the combined influence of trend characteristics and periodic characteristics, and improve the accuracy of geographical phenomenon analysis.

[0005] In a first aspect, an embodiment of the present application provides a method for identifying hotspots based on remote sensing images, comprising the following steps:

[0006] Acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset;

[0007] Decomposing the time series according to an ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set, wherein the target sequence includes an IMF subsequence;

[0008] According to the IMF subsequence and the hotspot index calculation algorithm, a hotspot index of a single pixel in the remote sensing image is obtained;

[0009] Acquire the spatial weight parameter of the remote sensing image, and acquire the hotspot area of ​​the target area according to the hotspot index and the spatial weight parameter;

[0010] In response to a hotspot display instruction, the hotspot display instruction includes a remote sensing image data set of the area to be identified, obtaining electronic map data, obtaining the hotspot area corresponding to the remote sensing image data set of the area to be identified based on the remote sensing image data set of the area to be identified, and displaying and marking the hotspot area on the electronic map data.

[0011] In a second aspect, an embodiment of the present application provides a hotspot identification device based on remote sensing images, comprising:

[0012] An acquisition module is used to acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset;

[0013] A sequence acquisition module, used for decomposing the time series according to an ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set, wherein the target sequence includes an IMF subsequence;

[0014] A hotspot index calculation module, used to obtain the hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm;

[0015] A hotspot acquisition module is used to acquire the spatial weight parameter of the remote sensing image, and acquire the hotspot of the target area according to the hotspot index and the spatial weight parameter;

[0016] A hotspot display module is used to respond to a hotspot display instruction, wherein the hotspot display instruction includes a remote sensing image data set of the area to be identified, obtain electronic map data, obtain the hotspot area corresponding to the remote sensing image data set of the area to be identified based on the remote sensing image data set of the area to be identified, and display and mark the hotspot area on the electronic map data.

[0017] In a third aspect, an embodiment of the present application provides a device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the hotspot identification method based on remote sensing images as described in the first aspect are implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the hotspot identification method based on remote sensing images as described in the first aspect are implemented.

[0019] In an embodiment of the present application, a hotspot identification method, apparatus, equipment and storage medium based on remote sensing images are provided, which uses a collective empirical mode decomposition algorithm to separate trend signals and periodic signals with nonlinear characteristics, and constructs a hotspot index based on the amount of information of the two to identify hotspots. The combined influence of trend characteristics and periodic characteristics is fully considered, the accuracy of geographical phenomenon analysis is improved, and the hotspots are displayed, which can visualize the spatial differentiation characteristics of geographical phenomena formed in the long-term evolution process, which is more convenient.

[0020] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a hotspot identification method based on remote sensing images provided in the first embodiment of the present application;

[0022] Figure 2 A schematic diagram of the process of S4 in the hotspot area identification method based on remote sensing images provided in the first embodiment of the present application;

[0023] Figure 3 A table of correspondence between the hotspot index, the Anselm local Moran index, the spatial hysteresis value and the cluster type provided in the first embodiment of the present application;

[0024] Figure 4 A spatial morphology diagram of different clustering types provided in the first embodiment of the present application;

[0025] Figure 5 A schematic diagram of a flow chart of a hotspot identification method based on remote sensing images provided in the second embodiment of the present application;

[0026] Figure 6 A schematic diagram of a process flow in a hotspot identification method based on remote sensing images provided in the third embodiment of the present application;

[0027] Figure 7 A table of correspondence between hotspot index, cluster type, trend change rate and hotspot type provided in the third embodiment of the present application;

[0028] Figure 8 A schematic diagram of a flow chart of a hotspot identification method based on remote sensing images provided in the fourth embodiment of the present application;

[0029] Fig. 9 A schematic diagram of the structure of a hotspot identification device based on remote sensing images provided in the fifth embodiment of the present application;

[0030] Fig.10 A schematic diagram of the structure of the device provided in the sixth embodiment of the present application. DETAILED DESCRIPTION

[0031] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0032] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0033] It should be understood that, although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0034] Based on the concept of spatiotemporal change hotspots, the hotspot area is a geographical phenomenon in the long-term evolution process. Based on the concept of spatiotemporal change hotspots, it specifically refers to the trend change value of the area being greater than a preset trend change threshold, wherein the trend change value represents the trend change accumulated in the long-term evolution process of the geographical phenomenon, and the trend change threshold can be changed according to the accuracy of the remote sensing image and the actual situation in different regions.

[0035] See also Figure 1 , Figure 1 A flowchart of a hotspot identification method based on remote sensing images provided in the first embodiment of the present application is provided, wherein the method comprises the following steps:

[0036] S1: Acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset.

[0037] The executor of the hotspot identification method based on remote sensing images is an identification device of the hotspot identification method based on remote sensing images (hereinafter referred to as the identification device). In an optional embodiment, the identification device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0038] In this embodiment, the recognition device obtains a remote sensing image dataset of the target area input by the user, and may also obtain a remote sensing image dataset of the target area from a database, wherein the remote sensing image dataset includes a plurality of remote sensing images, and each remote sensing image represents observation data of a satellite at a certain point in time.

[0039] According to the time points of the remote sensing images in the remote sensing image dataset, the time series of the remote sensing image dataset is defined to obtain the time series of the remote sensing image dataset, wherein the expression of the time series is:

[0040] G={g1,g2,…g i ,…,g n}

[0041] Where G is the time series of remote sensing image dataset, and g i represents the observation value of the remote sensing image in the remote sensing image dataset at time point i, and n represents the length of the time series.

[0042] S2: Decomposing the time series according to the ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set.

[0043] The target sequence includes an IMF subsequence and a trend sequence, the IMF (Intrinsic Mode Function) is an intrinsic mode function, and the IMF subsequence includes a seasonal sequence and an interannual sequence.

[0044] In this embodiment, the recognition device obtains a random noise sequence, and adds the random noise sequence to the original time sequence G as the original time sequence G1.

[0045] The time series is decomposed by using the ensemble empirical mode decomposition algorithm to obtain the IMF subsequence and trend sequence. The specific steps are as follows:

[0046] The upper envelope and the lower envelope of the original time series G1 are calculated, wherein the upper envelope is a sequence composed of local maximum values, and the lower envelope is a sequence composed of local minimum values. The average values ​​of the upper envelope and the lower envelope are calculated to obtain a mean sequence.

[0047] The original time series G1 is subtracted from the mean sequence to obtain a processed time series G2, and the time series G2 is analyzed to determine whether the time series G2 meets a preset IMF condition, wherein the IMF condition includes a first condition and a second condition, wherein the first condition is specifically that the number of local extreme points and zero-crossing points of the time series G2 is equal, or differs by at most one; and the second condition is specifically that at any time point, the average values ​​of the upper envelope and the lower envelope of the time series G2 must be zero.

[0048] When the time series G2 meets the preset IMF condition, the time series G2 is used as an IMF subsequence;

[0049] When the time series G2 does not meet the preset IMF condition, the time series G2 is subtracted from the time series G1 and the original time series G1 is replaced. The above steps are repeated until the newly acquired time series G2 meets the preset IMF condition, thereby obtaining an IMF subsequence.

[0050] The IMF subsequence is analyzed to determine whether the IMF subsequence satisfies a preset algorithm termination condition, wherein the algorithm termination condition is: the number of extreme values ​​contained in the IMF subsequence does not exceed two.

[0051] When the IMF subsequence meets the preset algorithm termination condition, the original time series G1 before the algorithm is executed is subtracted from the sum of the IMF subsequences to obtain a trend sequence;

[0052] When the IMF subsequence does not meet the preset algorithm termination condition, the time series G2 is subtracted from the time series G1 and the original time series G1 is replaced. The above steps are repeated until the newly obtained IMF subsequence meets the preset algorithm termination condition to obtain a trend sequence.

[0053] The obtained IMF subsequences are analyzed to obtain the number of peaks and the sequence length of the IMF subsequences, and the period of the IMF subsequences is obtained according to the number of peaks and the sequence length. When the period of the IMF subsequences does not exceed 12 months, they are merged into a seasonal sequence; when the period of the IMF subsequences exceeds 12 months, they are merged into an inter-annual sequence.

[0054] S3: Obtaining a hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm.

[0055] The hotspot index calculation algorithm is:

[0056]

[0057] In the formula, Ih is the hotspot index, σ() is the sequence variance function, G t is the trend sequence, G s is the seasonal sequence, G a An inter-annual series.

[0058] In this embodiment, the identification device obtains the hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm, which reflects the combined influence of the trend characteristics and periodic characteristics of a certain pixel of the remote sensing image in the remote sensing image data set, and more accurately identifies the hotspot area. The higher the value, the greater the proportion of the trend characteristics compared to the periodic characteristics, and the more likely it is a potential hotspot area.

[0059] S4: Obtain the spatial weight parameter of the remote sensing image, and obtain the hotspot area of ​​the target area according to the hotspot index and the spatial weight parameter.

[0060] The spatial weight parameter w i,j It can be obtained by calculating according to the distance between the pixel i position and the pixel j position of the remote sensing image using the Euclidean distance formula.

[0061] In this embodiment, the recognition device obtains the hotspot area of ​​the target area according to the obtained hotspot index and spatial weight parameter.

[0062] See also Figure 2 , Figure 2 The flowchart of S4 in the hotspot identification method based on remote sensing images provided in the first embodiment of the present application includes steps S401 to S402, which are specifically as follows:

[0063] S401: Obtaining the Anselm local Moran index of a single pixel in the remote sensing image according to the hotspot index, the spatial weight parameter and the Anselm local Moran index calculation algorithm.

[0064] The spatial weight parameter w of the remote sensing image i,j It represents the influence of the spatial distance between pixels in the hot spot area on the Anselm local Moran index. The closer the distance between the pixel at position i and the pixel at position j of the remote sensing image, the greater the distance between the pixel at position i and the pixel at position j. i,j The larger the value of , the greater the impact on the Anselm local Moran index.

[0065] The Anselm local Moran index calculation algorithm includes functions f1 to f2, which are as follows:

[0066]

[0067]

[0068] In the formula, i and j represent the i position and j position of the pixel of the remote sensing image respectively. i is the Anselm local Moran index, I h is the hotspot index, I hi represents the hotspot index of position i in the remote sensing image, represents the average value of the hotspot index of all pixels in the remote sensing image, w i,j is the spatial weight parameter between the pixel at position i and the pixel at position j of the remote sensing image, n is the total number of pixels in the remote sensing image, L i is the first intermediate variable.

[0069] In this embodiment, the recognition device obtains the spatial weight parameter of the remote sensing image, and obtains the Anselm local Moran index I of a single pixel in the remote sensing image according to the hotspot index, the spatial weight parameter and the Anselm local Moran index calculation algorithm. i , used to distinguish the high value and low value of the pixel of the remote sensing image, wherein the high value is the high pixel value and the low value is the low pixel value; for example, a certain pixel I i >0, the pixel has similar variation characteristics to the neighboring pixels, and there is a spatial aggregation effect between high-value pixels and high-value pixels or between low-value pixels and low-value pixels; when I i When <0, the pixel has opposite variation characteristics to the neighboring pixels, indicating an outlier, such as a high-value pixel surrounded by a low-value pixel, or a low-value pixel surrounded by a high-value pixel. This delineates the geographic boundaries of the hotspot area.

[0070] S401: performing cluster analysis on the hotspot area according to the hotspot index, the Anselm local Moran index and the spatial weight parameter, obtaining the cluster type of the hotspot area, and obtaining the hotspot area of ​​the target area.

[0071] The clustering types include HH type, LL type, HL type and LH type, wherein the HH type represents the clustering area of ​​high-value pixels and high-value pixels, the LL type represents the clustering area of ​​low-value pixels and low-value pixels, the HL type represents the abnormal area of ​​high-value pixels surrounded by low-value pixels, and the LH type represents the abnormal area of ​​low-value pixels surrounded by high-value pixels.

[0072] In this embodiment, the recognition device establishes a coordinate system with the change hotspot data as the horizontal axis and the corresponding spatial hysteresis value as the vertical axis, wherein the value of the change hotspot data is The spatial lag value is a concept proposed by Anselm in 1988, which represents the weighted average of the standardized observation values ​​of the surrounding adjacent objects, specifically:

[0073]

[0074] Please refer to Figure 3 as well as Figure 4 , Figure 3 A table of correspondence between the change hotspot data, the Anselm local Moran index, the spatial lag value and the cluster type provided in the first embodiment of the present application; Figure 4 A spatial morphology diagram of different clustering types provided in the first embodiment of the present application.

[0075] According to the hotspot index, the horizontal axis coordinate data of the hotspot area is obtained, and according to the changing hotspot data and the spatial weight parameter, the vertical axis coordinate data of the hotspot area is obtained;

[0076] According to the corresponding relationship between the change hotspot data, the Anselm local Moran index, the spatial lag value, and the cluster type, the cluster type of the hotspot area is obtained. In an optional embodiment, when the value of the change hotspot data is greater than 0, the spatial lag value is greater than 0, and the Anselm local Moran index is greater than 0, the cluster type of the hotspot area is judged to be HH type; when the value of the change hotspot data is less than 0, the spatial lag value is less than 0, and the Anselm local Moran index is greater than 0, the cluster type of the hotspot area is judged to be LL type; when the value of the change hotspot data is less than 0, the spatial lag value is greater than 0, and the Anselm local Moran index is less than 0, the cluster type of the hotspot area is judged to be LH type; when the value of the change hotspot data is greater than 0, the spatial lag value is less than 0, and the Anselm local Moran index is less than 0, the cluster type of the hotspot area is judged to be HL type;

[0077] According to the clustering type of the hotspot area, the spatial form of the hotspot area is obtained, thereby obtaining the hotspot area of ​​the target area.

[0078] S5: In response to a hotspot display instruction, the hotspot display instruction includes a remote sensing image dataset of the area to be identified, obtaining electronic map data, obtaining a hotspot area corresponding to the remote sensing image dataset of the area to be identified based on the remote sensing image dataset of the area to be identified, and displaying and marking the hotspot area on the electronic map data.

[0079] The hotspot display instruction is sent by the user device and received by the identification device.

[0080] In this embodiment, the recognition device obtains the hotspot display instruction sent by the user, responds, and obtains the electronic map data.

[0081] The classification device obtains the remote sensing image data set of the area to be identified according to the hot spot display instruction, obtains the hot spot area corresponding to the remote sensing image data set of the area to be identified, returns it to the display interface of the classification device, and displays and marks the hot spot area on the electronic map data.

[0082] See also Figure 5 , Figure 5 The flowchart of the hotspot identification method based on remote sensing images provided in the second embodiment of the present application includes step S6, which is before step S5 and is specifically as follows:

[0083] S6: Obtain statistics of the remote sensing image, and inspect the hotspot area of ​​the target area according to the statistics and a preset statistics threshold.

[0084] In order to further obtain hot spots with significant statistical significance and improve the recognition accuracy of the hot spot area in the target area, in this embodiment, the recognition device obtains the statistics of the remote sensing image according to the hot spot index, the Anselm local Moran index, the spatial weight parameter and the statistics calculation algorithm, wherein the statistics calculation algorithm is:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] In the formula, z i represents the statistic at position i, E(I i ) is the second intermediate variable, V(I i ) is the third intermediate variable, A i is the fourth intermediate variable, B i is the fifth intermediate variable, b i is the sixth intermediate variable, k and h represent the k position and h position of the pixel of the remote sensing image respectively.

[0093] According to a preset statistical threshold, the hotspot area of ​​the target area is inspected. When the statistic is greater than the statistical threshold, a successful inspection result is obtained and the hotspot area of ​​the target area is output. When the statistic is less than the statistical threshold, a failed inspection result is obtained and the hotspot area of ​​the target area is obtained again.

[0094] See also Figure 6 , Figure 6The flowchart of the hotspot identification method based on remote sensing images provided in the third embodiment of the present application also includes step S7, which is as follows:

[0095] S7: Performing linear regression processing on the trend items in the trend sequence to obtain the trend change rate, and obtaining the hotspot type of the hotspot area according to the cluster type and the trend change rate.

[0096] Perform linear regression processing on the trend items in the trend sequence to obtain the trend change rate.

[0097] The trend change rate (Rs) is the linear change rate of the trend sequence.

[0098] Please refer to Figure 7 , Figure 7 A table showing the correspondence between hotspot index, cluster type, trend change rate and hotspot type provided in the third embodiment of the present application.

[0099] In this embodiment, the identification device performs linear regression processing on the trend items in the trend sequence to obtain the trend change rate, and obtains the hotspot type of the hotspot area according to the corresponding relationship between the hotspot index, cluster type, trend change rate, and the hotspot type.

[0100] In an optional embodiment, when the clustering type of the hotspot area is HH type and the trend change rate Rs is greater than 0, the hotspot type of the hotspot area is judged to be an ascending type; when the clustering type of the hotspot area is HH type and the trend change rate Rs is less than 0, the hotspot type of the hotspot area is judged to be a descending type; when the clustering type of the hotspot area is HL type and the trend change rate Rs is greater than 0, the hotspot type of the hotspot area is judged to be an ascending type; when the clustering type of the hotspot area is HL type and the trend change rate Rs is less than 0, the hotspot type of the hotspot area is judged to be a descending type.

[0101] See also Figure 8 , Figure 8 The schematic diagram of the S5 process in the hotspot area identification method based on remote sensing images provided in the fourth embodiment of the present application further includes step S501, which is as follows:

[0102] S501: According to the hot spot area corresponding to the remote sensing image data set of the area to be identified, the hot spot type corresponding to the hot spot area is obtained, and the hot spot type is displayed and marked on the electronic map data.

[0103] In this embodiment, the recognition device responds to the hotspot display instruction, obtains the hotspot corresponding to the remote sensing image data set of the area to be identified, obtains the hotspot type corresponding to the hotspot based on the hotspot corresponding to the remote sensing image data set of the area to be identified, returns it to the display interface of the classification device, and displays and marks the hotspot type on the electronic map data.

[0104] Please refer to Fig. 9 , Fig. 9 A schematic diagram of the structure of a hotspot identification device based on remote sensing images provided in the fifth embodiment of the present application. The device can implement all or part of the hotspot identification device based on remote sensing images through software, hardware, or a combination of both. The device 9 includes:

[0105] An acquisition module 91 is used to acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset;

[0106] A sequence acquisition module 92 is used to decompose the time series according to the ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set, wherein the target sequence includes an IMF subsequence;

[0107] A hotspot index calculation module 93, used to obtain the hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm;

[0108] A hotspot acquisition module 94 is used to acquire the spatial weight parameter of the remote sensing image, and acquire the hotspot of the target area according to the hotspot index and the spatial weight parameter;

[0109] The hotspot display module 95 is used to respond to a hotspot display instruction, which includes a remote sensing image data set of the area to be identified, obtain electronic map data, obtain the hotspot area corresponding to the remote sensing image data set of the area to be identified based on the remote sensing image data set of the area to be identified, and display and mark the hotspot area on the electronic map data.

[0110] In this embodiment, a remote sensing image dataset of a target area is acquired through an acquisition module, and a time series of the remote sensing image dataset is acquired according to the time points of the remote sensing images in the remote sensing image dataset; a sequence acquisition module is used to decompose the time series according to a set empirical mode decomposition algorithm to acquire a target sequence of the remote sensing image dataset, wherein the target sequence includes an IMF subsequence; a hotspot index calculation module is used to acquire a hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm; a hotspot area acquisition module is used to acquire a spatial weight parameter of the remote sensing image, and a hotspot area of ​​the target area is acquired according to the hotspot index and the spatial weight parameter; a hotspot area display module is used to respond to a hotspot area display instruction, wherein the hotspot area display instruction includes a remote sensing image dataset of a to-be-identified area, acquire electronic map data, acquire a hotspot area corresponding to the remote sensing image dataset of the to-be-identified area according to the remote sensing image dataset of the to-be-identified area, and display and mark the hotspot area on the electronic map data. The ensemble empirical mode decomposition algorithm is used to separate trend signals and periodic signals with nonlinear characteristics. A hotspot index is constructed according to the amount of information between the two to identify hotspot areas. The combined influence of trend characteristics and periodic characteristics is fully considered, which improves the accuracy of geographical phenomenon analysis and displays hotspot areas. It can visualize the spatial differentiation characteristics of geographical phenomena formed in the long-term evolution process, which is more convenient.

[0111] Please refer to Fig.10 , Fig.10 The structural diagram of the device provided for the sixth embodiment of the present application, the device 10 includes: a processor 101, a memory 102, and a computer program 103 stored in the memory 102 and executable on the processor 101; the computer device can store multiple instructions, and the instructions are suitable for the processor 101 to load and execute the method steps of the above-mentioned embodiments one to four. The specific execution process can refer to the specific description of embodiments one to four, which will not be repeated here.

[0112] Among them, the processor 101 may include one or more processing cores. The processor 101 uses various interfaces and lines to connect various parts in the server, and executes various functions and processes data of the hot spot identification device 9 based on remote sensing images by running or executing instructions, programs, code sets or instruction sets stored in the memory 102, and calling the data in the memory 102. Optionally, the processor 101 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programble Logic Array, PLA). The processor 101 can integrate one or more combinations of a central processing unit 101 (Central Processing Unit, CPU), an image processor 101 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 101, and it can be implemented by a single chip.

[0113] Among them, the memory 102 may include a random access memory 102 (Random Access Memory, RAM), and may also include a read-only memory 102 (Read-Only Memory). Optionally, the memory 102 includes a non-transitory computer-readable storage medium. The memory 102 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 102 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 102 may also be optionally at least one storage device located away from the aforementioned processor 101.

[0114] An embodiment of the present application also provides a storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing the method steps of the above-mentioned embodiments one to four. The specific execution process can be found in the specific descriptions of embodiments one to four, which will not be repeated here.

[0115] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraint algorithm of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0118] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0121] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0122] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications to the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims and equivalent technologies of the present invention, the present invention is also intended to include these changes and modifications.

Claims

1. A hotspot identification method based on remote sensing images, characterized in that: The following steps are involved: Acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset; Decomposing the time series according to an ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set, wherein the target sequence includes an IMF subsequence; According to the IMF subsequence and the hotspot index calculation algorithm, a hotspot index of a single pixel in the remote sensing image is obtained; The spatial weight parameter of the remote sensing image is obtained, and the Anselm local Moran index of a single pixel in the remote sensing image is obtained according to the hotspot index, the spatial weight parameter and the Anselm local Moran index calculation algorithm, wherein the Anselm local Moran index calculation algorithm includes functions f1 to f2, which are specifically as follows: In the formula, i and j represent the i position and j position of the pixel of the remote sensing image respectively. i is the Anselm local Moran index, I h is the hotspot index, I hi Indicates the hotspot index of position i, represents the average value of the hotspot index of all pixels in the remote sensing image, w i,j is the spatial weight parameter of position i and position j, n is the total number of pixels in the remote sensing image, L i is the first intermediate variable; According to the hotspot index, the Anselm local Moran index and the spatial weight parameter, cluster analysis is performed on the hotspot area to obtain the cluster type of the hotspot area and obtain the hotspot area of ​​the target area; In response to a hotspot display instruction, the hotspot display instruction includes a remote sensing image data set of the area to be identified, obtaining electronic map data, obtaining the hotspot area corresponding to the remote sensing image data set of the area to be identified based on the remote sensing image data set of the area to be identified, and displaying and marking the hotspot area on the electronic map data.

2. The hotspot identification method based on remote sensing images according to claim 1 is characterized in that: Also includes the steps: The statistics of the remote sensing image are obtained, and the hotspot area of ​​the target area is tested according to the statistics and a preset statistics threshold.

3. The hotspot identification method based on remote sensing images according to claim 2 is characterized in that: The step of obtaining the statistics of the remote sensing image comprises the steps of: The statistics are obtained according to the hotspot index, the Anselm local Moran index, the spatial weight parameter and the statistics calculation algorithm, wherein the statistics calculation algorithm includes functions f3 to f9, which are as follows: In the formula, z i represents the statistic at position i, E(I i ) is the second intermediate variable, V(I i ) is the third intermediate variable, A i is the fourth intermediate variable, B i is the fifth intermediate variable, b i is the sixth intermediate variable, k and h represent the k position and h position of the pixel of the remote sensing image respectively.

4. The hotspot identification method based on remote sensing images according to claim 3 is characterized in that: The target sequence also includes a trend sequence.

5. The hotspot identification method based on remote sensing images according to claim 4 is characterized in that: Also includes the steps: Linear regression processing is performed on the trend items in the trend sequence to obtain the trend change rate, and the hotspot type of the hotspot area is obtained according to the cluster type and the trend change rate.

6. The hotspot identification method based on remote sensing images according to claim 5 is characterized in that: The step of responding to a hotspot display instruction, wherein the hotspot display instruction includes a remote sensing image data set of a region to be identified, acquiring electronic map data, acquiring a hotspot corresponding to the remote sensing image data set of the region to be identified according to the remote sensing image data set of the region to be identified, and displaying and marking the hotspot on the electronic map data, further comprises the following steps: According to the hot spot area corresponding to the remote sensing image data set of the area to be identified, the hot spot type corresponding to the hot spot area is obtained, and the hot spot type is displayed and marked on the electronic map data.

7. A hotspot identification device based on remote sensing images, characterized in that: include: An acquisition module is used to acquire a remote sensing image dataset of a target area, and acquire a time series of the remote sensing image dataset according to time points of remote sensing images in the remote sensing image dataset; A sequence acquisition module, used for decomposing the time series according to an ensemble empirical mode decomposition algorithm to obtain a target sequence of the remote sensing image data set, wherein the target sequence includes an IMF subsequence; A hotspot index calculation module, used to obtain the hotspot index of a single pixel in the remote sensing image according to the IMF subsequence and the hotspot index calculation algorithm; The hotspot area acquisition module is used to obtain the spatial weight parameter of the remote sensing image, and obtain the Anselm local Moran index of a single pixel in the remote sensing image according to the hotspot index, the spatial weight parameter and the Anselm local Moran index calculation algorithm, wherein the Anselm local Moran index calculation algorithm includes functions f1 to f2, which are as follows: In the formula, i and j represent the i position and j position of the pixel of the remote sensing image respectively. i is the Anselm local Moran index, I h is the hotspot index, I hi Indicates the hotspot index of position i, represents the average value of the hotspot index of all pixels in the remote sensing image, w i,j is the spatial weight parameter of position i and position j, n is the total number of pixels in the remote sensing image, L i is the first intermediate variable; According to the hotspot index, the Anselm local Moran index and the spatial weight parameter, cluster analysis is performed on the hotspot area to obtain the cluster type of the hotspot area and obtain the hotspot area of ​​the target area; A hotspot display module is used to respond to a hotspot display instruction, wherein the hotspot display instruction includes a remote sensing image data set of the area to be identified, obtain electronic map data, obtain the hotspot area corresponding to the remote sensing image data set of the area to be identified based on the remote sensing image data set of the area to be identified, and display and mark the hotspot area on the electronic map data.

8. A device, characterized in that include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the hotspot identification method based on remote sensing images as described in any one of claims 1 to 6 are implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the hot spot identification method based on remote sensing images as described in any one of claims 1 to 6 are implemented.

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