A geological identification method and system based on satellite remote sensing

Abstract

The application discloses a kind of geological identification method and system based on satellite remote sensing, it is related to remote sensing monitoring technical field.Target area panchromatic remote sensing image and multispectral image are collected, panchromatic remote sensing image and multispectral image are fused to obtain the image to be identified;According to the static change detection of the image to be identified and reference image, determine the change region;The image to be identified collected between current cycle and reference cycle is sorted into image to be identified group, and the change region is segmented to obtain change image group;Determine the flow velocity of light flow by dynamic change detection to change image group, and carry out geological hazard early warning.Through the combination of panchromatic remote sensing image and multispectral image, the image definition can be improved, and the image analysis is carried out in two stages, first, the static change detection is carried out to determine the change region, and then, the dynamic change detection is carried out to the change region, so that the amount of calculation required for image analysis can be greatly reduced while ensuring the accuracy and real-time of data analysis.

Description

Technical Field

[0001] This invention relates to the field of remote sensing monitoring technology, specifically to a geological identification method and system based on satellite remote sensing. Background Technology

[0002] Landslides are natural disasters with significant impacts. They typically originate from extreme natural events such as torrential rains, volcanic eruptions, and earthquakes, and are often associated with human activities such as deforestation and intensive land development for agricultural purposes. As a serious natural geological hazard, landslides can cause severe damage to property, infrastructure, and human safety if they occur where people and property are located.

[0003] With advancements in monitoring technology, automated monitoring and early warning methods can be better applied to landslide monitoring and early warning systems. These tools provide powerful capabilities to monitor changes in pre-landslide indicators and behavior, thereby reducing their danger, ensuring safety, and minimizing property damage. Among these, radar and optical remote sensing data are increasingly used to support landslide risk management efforts due to their multispectral and textural characteristics, high revisit periods, wide coverage, and high spatial resolution.

[0004] In existing technologies, landslide monitoring based on satellite remote sensing requires high image clarity. Moreover, due to the large volume and high diversity of remote sensing data, a huge amount of computation is required to ensure the accuracy and real-time performance of data analysis. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned above in the background technology, and to propose a geological identification method and system based on satellite remote sensing.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] The first aspect of this invention provides a geological identification method based on satellite remote sensing, the method comprising:

[0008] Panchromatic remote sensing images and multispectral images of the target area are periodically and synchronously acquired by satellite, and the panchromatic remote sensing images and multispectral images at the same time are fused to obtain the image to be identified.

[0009] Static change detection is performed on the image to be identified and the reference image in the current period to determine the changed region in the image to be identified in the current period; the reference image is the image of the target region to be identified during the reference period; the reference period is the preset number of periods before the current period;

[0010] The images to be identified collected between the current period and the reference period are sorted in chronological order to form a group of images to be identified, and the changing regions of each image in the group of images to be identified are segmented to obtain a group of changing images.

[0011] Dynamic change detection is performed on the changed image group to determine the light flow velocity in the changed area, and geological hazard warning is given for the target area based on the light flow velocity.

[0012] Optionally, the image to be identified is obtained by fusing panchromatic remote sensing images and multispectral images from the same time point, including:

[0013] The panchromatic remote sensing image and the multispectral image at the same time are decomposed into two levels by passing them through a preset filter to obtain the first bottom layer image and the first detail image of the panchromatic remote sensing image, as well as the second bottom layer image and the second detail image of the multispectral image.

[0014] A first saliency map of the first bottom layer image, a second saliency map of the first detail image, a third saliency map of the second bottom layer image, and a fourth saliency map of the second detail image are generated respectively.

[0015] For the pixels at the first target coordinates in the first saliency map and the third saliency map, the pixels with larger saliency values ​​are determined as the first target pixels, and all the first target pixels are combined to obtain the target bottom layer image; the first target coordinates are any coordinates at the same position in the first saliency map and the third saliency map;

[0016] For the pixels with second target coordinates in the second saliency map and the fourth saliency map, the pixels with larger saliency values ​​are selected as second target pixels, and all second target pixels are combined to obtain a target detail image; the second target coordinates are any coordinates at the same position in the second saliency map and the fourth saliency map;

[0017] The target bottom image and the target detail image are combined to obtain the image to be identified.

[0018] Optionally, the preset filter is a guide filter or a fast guide filter; the panchromatic remote sensing image and the multispectral image serve as guide images for each other.

[0019] Optionally, static change detection is performed based on the image to be identified and the reference image in the current period to determine the changed regions in the image to be identified in the current period, including:

[0020] The difference image is obtained by subtracting the reference image from the image to be identified in the current period, and the pixels in the difference image that exceed a preset threshold are taken as the observed pixels.

[0021] The observed pixels are grouped according to distance to obtain multiple pixel groups;

[0022] For each pixel group, the smallest bounding box containing all observed pixels in that pixel group is determined as the region of change.

[0023] Optionally, performing dynamic change detection on the changed image group to determine the light flow velocity in the changed area, and providing geological hazard warning for the target area based on the light flow velocity includes:

[0024] The changing image group is divided into multiple image subgroups using a preset time window; the preset time window has a duration of three cycles.

[0025] For each image subgroup, the deformation of the observed pixels is calculated, and the deformation of each image subgroup is used as a function of time as the optical flow velocity function to determine the optical flow velocity of the target region in the current period.

[0026] A geological hazard alarm is triggered when the light flow velocity exceeds a preset alarm threshold.

[0027] A second aspect of the present invention also provides a geological identification system based on satellite remote sensing, comprising:

[0028] The image acquisition module is used to periodically and synchronously acquire panchromatic remote sensing images and multispectral images of the target area through satellite, and to fuse the panchromatic remote sensing images and multispectral images at the same time to obtain the image to be identified;

[0029] The static change detection module is used to perform static change detection based on the image to be identified and a reference image in the current period, and to determine the changed region in the image to be identified in the current period; the reference image is the image of the target region to be identified during the reference period; the reference period is the preset number of periods before the current period;

[0030] The segmentation module is used to sort the images to be identified acquired between the current period and the reference period according to the chronological order to form an image group to be identified, and to segment the changing regions of each image in the image group to be identified to obtain a changing image group.

[0031] The dynamic change detection module is used to perform dynamic change detection on the changed image group to determine the light flow velocity in the changed area, and to provide geological hazard warning for the target area based on the light flow velocity.

[0032] Optionally, the image acquisition module includes an image fusion module; the image fusion module includes:

[0033] The two-level decomposition module is used to decompose the panchromatic remote sensing image and the multispectral image at the same time into two levels through preset filters, so as to obtain the first bottom layer image and the first detail image of the panchromatic remote sensing image, and the second bottom layer image and the second detail image of the multispectral image.

[0034] The generation module saliency map is used to generate a first saliency map of the first bottom layer image, a second saliency map of the first detail image, a third saliency map of the second bottom layer image, and a fourth saliency map of the second detail image, respectively.

[0035] The first fusion module is used to determine the pixel with the larger pixel significance value as the first target pixel for the pixels of the first target coordinates in the first saliency map and the third saliency map, and combine all the first target pixels to obtain the target bottom layer image; the first target coordinates are any coordinates of the same position in the first saliency map and the third saliency map;

[0036] The second fusion module is used to select the pixel with the larger pixel significance value as the second target pixel for the pixels of the second target coordinates in the second saliency map and the fourth saliency map, and combine all the second target pixels to obtain the target detail image; the second target coordinates are any coordinates of the same position in the second saliency map and the fourth saliency map;

[0037] The third fusion module is used to combine the target low-level image and the target detail image to obtain the image to be identified.

[0038] Optionally, the preset filter is a guide filter or a fast guide filter; the panchromatic remote sensing image and the multispectral image serve as guide images for each other.

[0039] Optionally, the static change detection module includes:

[0040] The observation pixel determination module is used to subtract the reference image from the image to be identified in the current period to obtain a difference image, and to take the pixels in the difference image that exceed a preset threshold as observation pixels.

[0041] The first grouping module is used to group the observed pixels according to the distance to obtain multiple pixel groups;

[0042] The change region determination module is used to determine the smallest rectangular box containing all observed pixels in each pixel group as the change region.

[0043] Optionally, the dynamic change detection module includes:

[0044] The second grouping module is used to divide the changing image group into multiple image subgroups using a preset time window; the preset time window has a duration of three cycles.

[0045] The optical flow velocity calculation module is used to calculate the deformation of the observed pixels for each image subgroup, establish a function of time for the deformation of each image subgroup as the optical flow velocity function, and determine the optical flow velocity of the target region in the current period.

[0046] An alarm module is used to trigger a geological hazard alarm when the light flow speed exceeds a preset alarm threshold.

[0047] The beneficial effects of this invention are:

[0048] This invention provides a geological identification method based on satellite remote sensing. It involves periodically and synchronously acquiring panchromatic and multispectral remote sensing images of a target area via satellite, fusing these images at the same time to obtain an image to be identified. Static change detection is performed on the image to be identified in the current period and a reference image to determine the changed regions within the current period's image. The reference image is the image of the target area to be identified during a reference period, which is a preset number of periods prior to the current period. The images to be identified acquired between the current and reference periods are arranged chronologically to form an image group. The changed regions in each image within this group are segmented to obtain a changed image group. Dynamic change detection is performed on the changed image group to determine the light flow velocity in the changed regions, and geological hazard warnings for the target area are issued based on this light flow velocity. By combining panchromatic and multispectral images, image clarity can be improved. By performing image analysis in two stages—first static change detection to determine the changed regions, and then dynamic change detection—the accuracy and real-time performance of data analysis can be ensured while significantly reducing the computational load required for image analysis. Attached Figure Description

[0049] The invention will now be further described with reference to the accompanying drawings.

[0050] Figure 1 A flowchart illustrating a geological identification method based on satellite remote sensing, provided as an embodiment of the present invention;

[0051] Figure 2 This is a system block diagram of a geological identification system based on satellite remote sensing, provided for an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] This invention provides a geological identification method based on satellite remote sensing. See also... Figure 1 , Figure 1 A flowchart illustrating a geological identification method based on satellite remote sensing, provided as an embodiment of the present invention.

[0054] The methods include:

[0055] S1 uses satellites to periodically and synchronously acquire panchromatic remote sensing images and multispectral images of the target area, and then fuses the panchromatic remote sensing images and multispectral images at the same time to obtain the image to be identified.

[0056] S2, based on the image to be identified and the reference image in the current period, perform static change detection to determine the changed areas in the image to be identified in the current period.

[0057] S3: Sort the images to be identified acquired between the current cycle and the reference cycle in chronological order to form a group of images to be identified, and segment the changing regions of each image in the group of images to be identified to obtain a group of changing images.

[0058] S4 performs dynamic change detection on the changing image group to determine the light flow velocity in the changed area, and provides geological hazard warning for the target area based on the light flow velocity.

[0059] The reference image is the image of the target region to be identified during the reference period; the reference period is the preset number of periods before the current period.

[0060] The geological identification method based on satellite remote sensing provided in this invention can improve image clarity by combining panchromatic remote sensing images and multispectral images. By performing image analysis in two stages—first, static change detection to determine the change area, and then dynamic change detection of the change area—the computational load required for image analysis can be greatly reduced while ensuring the accuracy and real-time performance of data analysis.

[0061] In one implementation, the satellite's data acquisition cycle can typically be one day, two days, or one week, etc., and the acquisition time for each cycle can be kept at the same time every day, or at the same time every day when the sun's angle of illumination is the same, to reduce the impact of changes in light and shadow on the images. The acquisition cycle can be determined based on the geological and environmental attributes of the target area. Geological attributes include the material of the mountains in the target area and whether the target area is located in an earthquake zone, etc. Weather attributes include the average annual precipitation and forest coverage of the target area, etc. For example, the higher the average annual precipitation, the shorter the acquisition cycle; or the higher the forest coverage, the longer the acquisition cycle; or the closer the target area is to an earthquake zone, the shorter the acquisition cycle; or the more easily the mountain material in the target area is corroded, the shorter the acquisition cycle, etc.

[0062] In one embodiment, image fusion of a panchromatic remote sensing image and a multispectral image at the same time to obtain the image to be identified includes:

[0063] Step 1: The panchromatic remote sensing image and the multispectral image at the same time are decomposed into two levels by passing them through a preset filter to obtain the first bottom layer image and the first detail image of the panchromatic remote sensing image, as well as the second bottom layer image and the second detail image of the multispectral image.

[0064] Step 2: Generate the first saliency map of the first bottom layer image, the second saliency map of the first detail image, the third saliency map of the second bottom layer image, and the fourth saliency map of the second detail image, respectively.

[0065] Step 3: For the pixels at the first target coordinates in the first saliency map and the third saliency map, determine the pixels with larger saliency values ​​as the first target pixels, and combine all the first target pixels to obtain the target bottom layer image; the first target coordinates are any coordinates at the same position in the first saliency map and the third saliency map;

[0066] Step 4: For the pixels with the second target coordinates in the second saliency map and the fourth saliency map, select the pixels with larger saliency values ​​as the second target pixels, and combine all the second target pixels to obtain the target detail image; the second target coordinates are any coordinates at the same position in the second saliency map and the fourth saliency map;

[0067] Step 5: Combine the target bottom image and the target detail image to obtain the image to be identified.

[0068] In one implementation, the saliency maps of each image can be generated using any method in the prior art, and there is no limitation herein. For example, algorithms such as Itti, SR, FT, HC, CA, and GR can be used.

[0069] In one embodiment, the preset filter is a guide filter or a fast guide filter; the panchromatic remote sensing image and the multispectral image serve as guide images for each other.

[0070] In one implementation, panchromatic remote sensing images and multispectral images serve as guide maps for each other, which can reduce noise in each image and improve image detection accuracy.

[0071] In one embodiment, static change detection is performed based on the image to be identified and a reference image in the current period to determine the changed regions in the image to be identified in the current period, including:

[0072] Step 1: Subtract the reference image from the image to be identified in the current period to obtain the difference image. Pixels in the difference image that exceed a preset threshold are taken as observation pixels.

[0073] Step two involves grouping the observed pixels into multiple pixel groups based on distance.

[0074] Step 3: For each pixel group, determine the smallest rectangular box containing all observed pixels in that pixel group as the region of change.

[0075] In one embodiment, dynamic change detection of a changing image set is performed to determine the light flow velocity in the changed area, and geological hazard warning for the target area is given based on the light flow velocity, including:

[0076] Step 1: Divide the changing image group into multiple image subgroups using a preset time window; the preset time window has a duration of three cycles.

[0077] Step 2: For each image subgroup, calculate the deformation of the observed pixels, establish a time-dependent function for the deformation of each image subgroup as the light flow velocity function, and determine the light flow velocity of the target region in the current period.

[0078] Step 3: When the light flow speed exceeds the preset alarm threshold, a geological hazard alarm is triggered.

[0079] In one implementation, the light flow velocity function can calculate the light flow velocity in the current cycle and predict the light flow velocity in future cycles. Geological hazard alarms can also be triggered based on the predicted light flow velocity in future cycles.

[0080] This invention provides a geological identification system based on satellite remote sensing. (See also...) Figure 2 , Figure 2 A system block diagram of a satellite remote sensing-based geological identification system provided in this embodiment of the invention includes:

[0081] The image acquisition module is used to periodically and synchronously acquire panchromatic remote sensing images and multispectral images of the target area through satellite, and to fuse the panchromatic remote sensing images and multispectral images at the same time to obtain the image to be identified;

[0082] The static change detection module is used to perform static change detection based on the image to be identified and the reference image in the current period, and to determine the changed region in the image to be identified in the current period; the reference image is the image of the target region to be identified during the reference period; the reference period is the preset number of periods before the current period;

[0083] The segmentation module is used to sort the images to be identified acquired between the current period and the reference period according to the chronological order to form a group of images to be identified, and to segment the changing regions of each image in the group of images to be identified to obtain a group of changing images.

[0084] The dynamic change detection module is used to detect dynamic changes in a group of changing images to determine the light flow velocity in the changed area, and to provide early warning of geological hazards in the target area based on the light flow velocity.

[0085] The geological identification system based on satellite remote sensing provided in this invention can improve image clarity by combining panchromatic remote sensing images and multispectral images. By performing image analysis in two stages—first, static change detection to determine the change area, and then dynamic change detection of the change area—the system can greatly reduce the amount of computation required for image analysis while ensuring the accuracy and real-time performance of data analysis.

[0086] In one embodiment, the image acquisition module includes an image fusion module; the image fusion module includes:

[0087] The two-level decomposition module is used to decompose the panchromatic remote sensing image and the multispectral image at the same time into two levels through preset filters, so as to obtain the first bottom layer image and the first detail image of the panchromatic remote sensing image, and the second bottom layer image and the second detail image of the multispectral image.

[0088] The generation module saliency map is used to generate a first saliency map of the first bottom layer image, a second saliency map of the first detail image, a third saliency map of the second bottom layer image, and a fourth saliency map of the second detail image, respectively.

[0089] The first fusion module is used to determine the pixel with the larger pixel significance value as the first target pixel for the pixels of the first target coordinates in the first saliency map and the third saliency map, and combine all the first target pixels to obtain the target bottom layer image; the first target coordinates are any coordinates of the same position in the first saliency map and the third saliency map;

[0090] The second fusion module is used to select the pixel with the larger saliency value as the second target pixel from the pixels of the second target coordinates in the second saliency map and the fourth saliency map, and combine all the second target pixels to obtain the target detail image; the second target coordinates are any coordinates at the same position in the second saliency map and the fourth saliency map;

[0091] The third fusion module is used to combine the target's low-level image and the target's detail image to obtain the image to be identified.

[0092] In one embodiment, the preset filter is a guide filter or a fast guide filter; the panchromatic remote sensing image and the multispectral image serve as guide images for each other.

[0093] In one embodiment, the static change detection module includes:

[0094] The observation pixel determination module is used to subtract the reference image from the image to be identified in the current period to obtain the difference image, and to take the pixels in the difference image that exceed the preset threshold as observation pixels.

[0095] The first grouping module is used to group the observed pixels according to the distance to obtain multiple pixel groups;

[0096] The change region determination module is used to determine the smallest rectangular box containing all observed pixels in each pixel group as the change region.

[0097] In one embodiment, the dynamic change detection module includes:

[0098] The second grouping module is used to divide the changing image group into multiple image subgroups using a preset time window; the preset time window has a duration of three cycles.

[0099] The optical flow velocity calculation module is used to calculate the deformation of the observed pixels for each image subgroup, establish a function of time for the deformation of each image subgroup as the optical flow velocity function, and determine the optical flow velocity of the target area in the current period.

[0100] The alarm module is used to trigger a geological hazard alarm when the speed of light flow exceeds a preset alarm threshold.

[0101] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0103] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A satellite remote sensing-based geological identification method, characterized by, The method comprises: Periodically synchronously collecting full-color remote sensing images and multi-spectral images of a target region by a satellite, and fusing the full-color remote sensing images and the multi-spectral images at the same time to obtain a to-be-identified image; Performing static change detection on the to-be-identified image of the current period and a reference image to determine a change region in the to-be-identified image of the current period; The reference image is a to-be-identified image of the target region during a reference period; the reference period is a preset number of periods before the current period; Sorting the to-be-identified images collected between the current period and the reference period in chronological order to form a to-be-identified image group, and segmenting the change regions of the images in the to-be-identified image group to obtain a change image group; Performing dynamic change detection on the change image group to determine the optical flow velocity of the change region, and performing geological hazard early warning on the target region according to the optical flow velocity; The static change detection on the to-be-identified image of the current period and the reference image to determine the change region in the to-be-identified image of the current period comprises: Subtracting the reference image from the to-be-identified image of the current period to obtain a difference image, and regarding the pixel points in the difference image that exceed a preset threshold as observation pixel points; Grouping the observation pixel points according to distance to obtain a plurality of pixel groups; For each pixel group, determining a smallest rectangular frame containing all observation pixel points of the pixel group as a change region; The dynamic change detection on the change image group to determine the optical flow velocity of the change region, and the geological hazard early warning on the target region according to the optical flow velocity comprises: Dividing the change image group into a plurality of image subgroups by using a preset time window; the time length of the preset time window is three periods; For each image subgroup, calculating the deformation amount of the observation pixel points, establishing a function about time for the deformation amount corresponding to each image subgroup as an optical flow velocity function, and determining the optical flow velocity of the target region in the current period; When the optical flow velocity exceeds a preset alarm threshold, performing geological hazard alarm.

2. The method according to claim 1, wherein, The image fusion of the full-color remote sensing images and the multi-spectral images at the same time to obtain the to-be-identified image comprises: Performing two-level decomposition on the full-color remote sensing images and the multi-spectral images at the same time by using a preset filter respectively to obtain a first base layer image and a first detail image of the full-color remote sensing images and a second base layer image and a second detail image of the multi-spectral images; Generating a first saliency map of the first base layer image, a second saliency map of the first detail image, a third saliency map of the second base layer image, and a fourth saliency map of the second detail image respectively; For the pixels of a first target coordinate in the first saliency map and the third saliency map, determining a pixel with a larger pixel saliency value as a first target pixel, and combining all first target pixels to obtain a target base layer image; the first target coordinate is any one coordinate at the same position in the first saliency map and the third saliency map. For pixels of a second target coordinate in the second saliency map and the fourth saliency map, pixels with a larger pixel saliency value are taken as second target pixels, and all the second target pixels are combined to obtain a target detail image; the second target coordinate is any one of coordinates at the same position in the second saliency map and the fourth saliency map; The target base layer image and the target detail image are combined to obtain the to-be-recognized image.

3. The method according to claim 2, wherein, The preset filter is a guided filter or a fast guided filter; the panchromatic remote sensing image and the multispectral image are mutual guide images.

4. A satellite remote sensing-based geological identification system, characterized by, The method comprises: An image acquisition module is configured to periodically and synchronously acquire a panchromatic remote sensing image and a multispectral image of a target region by a satellite, and to perform image fusion on the panchromatic remote sensing image and the multispectral image at the same time to obtain a to-be-recognized image; A static change detection module is configured to perform static change detection on a current period to-be-recognized image and a reference image to determine a change region in the current period to-be-recognized image; The reference image is a to-be-recognized image of the target region during a reference period; The reference period is a preset number of periods before the current period; A segmentation module is configured to sort to-be-recognized images acquired between the current period and the reference period in chronological order to form a to-be-recognized image group, and to segment change regions of the images in the to-be-recognized image group to obtain a change image group; A dynamic change detection module is configured to perform dynamic change detection on the change image group to determine a light flow velocity of the change region, and to perform a geological hazard warning on the target region according to the light flow velocity; The static change detection module comprises: An observation pixel point determination module is configured to subtract the reference image from the current period to-be-recognized image to obtain a difference amplitude image, and to take pixel points exceeding a preset threshold in the difference amplitude image as observation pixel points; A first grouping module is configured to group the observation pixel points according to distances to obtain a plurality of pixel groups; A change region determination module is configured to determine, for each pixel group, a smallest rectangular frame containing all observation pixel points of the pixel group as a change region; The dynamic change detection module comprises: A second grouping module is configured to divide the change image group into a plurality of image subgroups using a preset time window; the preset time window has a time length of three periods; A light flow velocity calculation module is configured to calculate, for each image subgroup, a deformation amount of the observation pixel points, to establish a function about time as a light flow velocity function according to the deformation amounts of the image subgroups, and to determine a light flow velocity of the target region in the current period; An alarm module is configured to perform a geological hazard warning when the light flow velocity exceeds a preset alarm threshold.

5. The satellite remote sensing based geological identification system as claimed in claim 4, wherein, The image acquisition module comprises an image fusion module; the image fusion module comprises: A two-stage decomposition module is configured to perform two-stage decomposition on the panchromatic remote sensing image and the multispectral image at the same time by using a preset filter to obtain a first base layer image and a first detail image of the panchromatic remote sensing image, and a second base layer image and a second detail image of the multispectral image; The generating module generates a saliency map, which is used to generate a first saliency map of the first base layer image, a second saliency map of the first detail image, a third saliency map of the second base layer image, and a fourth saliency map of the second detail image, respectively; The first fusion module is used to determine a pixel with a larger pixel saliency value as a first target pixel for a pixel of a first target coordinate in the first saliency map and the third saliency map, and combine all the first target pixels to obtain a target base layer image; the first target coordinate is any one coordinate of the same position in the first saliency map and the third saliency map; The second fusion module is used to determine a pixel with a larger pixel saliency value as a second target pixel for a pixel of a second target coordinate in the second saliency map and the fourth saliency map, and combine all the second target pixels to obtain a target detail image; the second target coordinate is any one coordinate of the same position in the second saliency map and the fourth saliency map; The third fusion module is used to combine the target base layer image and the target detail image to obtain the to-be-recognized image.

6. The satellite remote sensing based geological identification system as claimed in claim 5, wherein, The preset filter is a guided filter or a fast guided filter; the panchromatic remote sensing image and the multispectral image are guide images of each other.

Images