Accumulated snow information extraction method and device, electronic equipment and medium

Abstract

The invention provides an accumulated snow information extraction method and device, electronic equipment and a medium, and the method comprises the steps: obtaining a remote sensing image of to-be-extracted accumulated snow information, and carrying out the preprocessing of the remote sensing image; calculating an accumulated snow index and a cirrus cloud index of a specified wave band of each pixel of the preprocessed remote sensing image; and determining an accumulated snow area based on the accumulated snow index and the cirrus cloud index. According to the method, the accumulated snow information in the emergency disaster reduction second satellite data can be effectively extracted, and the accuracy of accumulated snow extraction is improved.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and in particular to a method, apparatus, electronic device, and medium for extracting snow cover information. Background Technology

[0002] Snow cover is one of the most important surface coverings on Earth and a significant factor influencing the interaction between the global atmosphere and the Earth's surface. Heavy snowfall can lead to snow disasters, severely impacting the survival and health of humans and livestock, and causing disruptions and losses to transportation, communication, agriculture, and power generation. Therefore, the research and monitoring of snow cover is becoming increasingly important.

[0003] Currently, there are three main methods for obtaining snow cover information: obtaining snow cover information through ground stations, obtaining snow cover information using visible light, and obtaining snow cover information using microwave remote sensing. In recent decades, the technology for obtaining snow cover information using microwave remote sensing has developed rapidly. Due to its wide coverage and high efficiency in obtaining snow cover information, it has been widely used. Traditional microwave remote sensing snow cover extraction technology mainly uses the Normalized Snow Index (NDSI) to extract snow cover. This method utilizes the physical characteristics of snow cover—strong reflectivity in the visible light band (0.545–0.565 μm) and low reflectivity in the near-infrared band (1.628–1.652 μm)—for normalization processing, distinguishing snow-covered areas from cloud cover and non-snow-covered surface areas, while also eliminating some atmospheric influences. However, because the infrared data band range of the Emergency Disaster Reduction Satellite 2 is 0.63–1.68 μm, lacking a green light band, the traditional Normalized Snow Index (NDSI) is not applicable to Emergency Disaster Reduction Satellite 2 data and cannot effectively extract snow cover information from this remote sensing image. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus, electronic device and medium for extracting snow cover information, so as to effectively extract snow cover information from the data of Emergency Disaster Reduction Satellite 2 and improve the accuracy of snow cover extraction.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows: In a first aspect, embodiments of the present invention provide a method for extracting snow cover information, comprising: acquiring a remote sensing image from which snow cover information is to be extracted, and preprocessing the remote sensing image; calculating the snow cover index and cirrus index for a specified band of each pixel in the preprocessed remote sensing image; and determining the snow cover area based on the snow cover index and cirrus index.

[0006] In one embodiment, preprocessing the remote sensing image includes: correcting the remote sensing image based on predetermined calibration coefficients to convert the DN value of the remote sensing image into a radiometric value; and correcting the remote sensing image based on an atmospheric radiative transfer model to convert the radiometric value into a surface reflectance value.

[0007] In one embodiment, before calculating the snow cover index and cirrus index for each pixel of the preprocessed remote sensing image in a specified band, the method further includes: extracting the reflectance value of each pixel of the remote sensing image in a specified band; wherein the specified band includes: a first band, a third band, a fourth band, and a fifth band.

[0008] In one implementation, calculating the snow cover index and cirrus index for each pixel of the preprocessed remote sensing image in a specified band includes: calculating a first snow cover index for each pixel based on the reflectance values ​​of each pixel in the first and fifth bands of the preprocessed remote sensing image; calculating a second snow cover index for each pixel based on the reflectance values ​​of each pixel in the first and fourth bands of the preprocessed remote sensing image; and calculating a cirrus index for each pixel based on the reflectance values ​​of each pixel in the third and fifth bands of the preprocessed remote sensing image.

[0009] In one implementation, a first snow index is calculated for each pixel based on the reflectance values ​​of each pixel in the first and fifth bands of the preprocessed remote sensing image, including: calculating the first snow index according to the following formula: NDSI = (R1 - R5) / (R1 + R5) Wherein, NDSI represents the first snow index, R1 represents the reflectance value of the first band, and R5 represents the reflectance value of the fifth band. Based on the reflectance values ​​of each pixel in the first and fourth bands of the preprocessed remote sensing image, the second snow index for each pixel is calculated, including: calculating the second snow index according to the following formula: NDSII = (R1 - R4) / (R1 + R4) NDSII represents the second snow index, and R4 represents the reflectance value of the fourth band.

[0010] In one implementation, the cirrus index for each pixel is calculated based on the reflectance values ​​of each pixel in the third and fifth bands of the preprocessed remote sensing image, including: calculating the cirrus index according to the following formula: C = R3 / R5 Where C represents the cirrus index, R3 represents the reflectance value of the third band, and R5 represents the reflectance value of the fifth band.

[0011] In one implementation, determining a snow-covered area based on a snow index and a cirrus index includes: identifying pixels in a remote sensing image that have a first snow index greater than a first threshold, a second snow index greater than a second threshold, a cirrus index greater than a third threshold, a reflectance value in a first band greater than a fourth threshold, and a reflectance value in a fifth band less than a fourth threshold as snow-covered areas.

[0012] Secondly, embodiments of the present invention provide a snow cover information extraction device, comprising: a preprocessing module for acquiring remote sensing images of snow cover information to be extracted and preprocessing the remote sensing images; a calculation module for calculating the snow cover index and cirrus index of each pixel of the preprocessed remote sensing image in a specified band; and a snow cover area determination module for determining the snow cover area based on the snow cover index and cirrus index.

[0013] Thirdly, embodiments of the present invention provide an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of any of the methods provided in the first aspect above.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method provided in any of the first aspects above.

[0015] The embodiments of the present invention bring the following beneficial effects: The snow cover information extraction method, apparatus, electronic device, and medium provided in this invention can first acquire remote sensing images from which snow cover information is to be extracted, and then preprocess the remote sensing images; then calculate the snow cover index and cirrus cloud index for each pixel of the preprocessed remote sensing image in a specified band; finally, determine the snow cover area based on the snow cover index and cirrus cloud index. The above method, by using the snow cover index and cirrus cloud index in a specified band, can effectively extract snow cover information from remote sensing images in the Emergency Disaster Reduction Satellite 2 data, while also removing easily confused non-snow cover information such as high-level cirrus clouds from the remote sensing images, thus improving the accuracy of snow cover extraction.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for extracting snow cover information provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a snow extraction method based on remote sensing images from an infrared camera on the Emergency Disaster Reduction Satellite-2, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of a snow cover information extraction device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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, 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.

[0021] Currently, there are three main ways to obtain snow cover information: through ground stations, through visible light, and through microwave remote sensing. However, because the infrared data band of the Emergency Disaster Reduction Satellite 2 (EDS-2) ranges from 0.63 to 1.68 μm and lacks a green light band, the traditional Normalized Snow Index (NDSI) cannot be applied to EDS-2 data and cannot effectively extract snow cover information from the remote sensing images.

[0022] Based on this, the snow cover information extraction method, device, electronic device and medium provided in this embodiment of the invention can effectively extract snow cover information from Emergency Disaster Reduction Satellite 2 data and improve the accuracy of snow cover extraction.

[0023] To facilitate understanding of this embodiment, a method for extracting snow cover information disclosed in this invention will first be described in detail. This method can be executed by an electronic device, such as a smartphone, computer, or iPad. See also Figure 1 The flowchart shown illustrates a method for extracting snow cover information, which mainly includes the following steps S101 to S103: Step S101: Acquire remote sensing images of the snow cover information to be extracted and preprocess the remote sensing images.

[0024] In one implementation, remote sensing images from the infrared camera of the Emergency Disaster Reduction Satellite-2 are acquired as the remote sensing images from which snow cover information is to be extracted. To improve the accuracy of snow cover information extraction, the remote sensing images can be preprocessed after acquisition. Specifically, preprocessing of the remote sensing images can be performed using methods including, but not limited to, the following: First, the remote sensing image is corrected based on a predetermined calibration coefficient, converting the DN value of the remote sensing image into a radiometric value.

[0025] In practice, for the acquired remote sensing images, radiometric correction can be performed using pre-determined calibration coefficients to convert the DN value (a quantized value of the radiation received by the satellite, the magnitude of which is related to the quantization depth) of the remote sensing image into a radiometric value. Specifically, a linear conversion formula, along with offset and gain parameters, can be used to complete the conversion.

[0026] Then, the remote sensing images are corrected based on the atmospheric radiative transfer model, converting the radiative values ​​into surface reflectance values.

[0027] In practice, after converting the DN values ​​of remote sensing images into radiative values, atmospheric correction can be performed on the remote sensing images using atmospheric radiative transfer models to convert the radiative values ​​of the upper atmosphere into surface reflectance values. Specifically, atmospheric radiative transfer models include: the 6S model, the Lowtran model, the MODTRAN model, and the ATCOR model.

[0028] Step S102: Calculate the snow cover index and cirrus index for each pixel of the preprocessed remote sensing image in a specified band.

[0029] In one implementation, after obtaining the preprocessed remote sensing image, the reflectance value of each pixel in the remote sensing image in a specified band can be extracted first; wherein, the specified band includes: the first band (red light band), the third band (near infrared band), the fourth band (shortwave infrared band) and the fifth band (shortwave infrared band), and the band division is based on the infrared camera of the Emergency Disaster Reduction Satellite-2; then, the snow cover index and cirrus index are calculated based on the reflectance value of each pixel in the specified band.

[0030] Step S103: Determine the snow cover area based on the snow cover index and cirrus cloud index.

[0031] In one implementation, thresholds for the snow cover index and cirrus index, as well as thresholds for the reflectance values ​​of each specified band, can be preset. By comparing the relationship between the snow cover index, cirrus index, and the reflectance values ​​of each specified band with the corresponding thresholds, it can be determined whether each pixel in the remote sensing image belongs to a snow-covered area.

[0032] The snow information extraction method provided in this embodiment of the invention can effectively extract snow information from remote sensing images in Emergency Disaster Reduction Satellite 2 data by using snow index and cirrus cloud index of specified bands. At the same time, it can remove easily confused non-snow information such as high-level cirrus clouds in remote sensing images, thereby improving the accuracy of snow extraction.

[0033] In one implementation, for the aforementioned step S102, i.e., when calculating the snow cover index and cirrus index for a specified band of each pixel in the preprocessed remote sensing image, the following methods may be used, including but not limited to: First, based on the reflectance values ​​of each pixel in the preprocessed remote sensing image in the first and fifth bands, the first snow index for each pixel is calculated.

[0034] Specifically, the first snow index can be calculated using the following formula: NDSI = (R1 - R5) / (R1 + R5) Wherein, NDSI represents the first snow index, R1 represents the reflectance value of the first band, and R5 represents the reflectance value of the fifth band. Then, based on the reflectance values ​​of each pixel in the preprocessed remote sensing image in the first and fourth bands, the second snow index for each pixel is calculated.

[0035] Specifically, the second snow index can be calculated using the following formula: NDSII = (R1 - R4) / (R1 + R4) NDSII represents the second snow index, and R4 represents the reflectance value of the fourth band.

[0036] Finally, based on the reflectance values ​​of each pixel in the preprocessed remote sensing image in the third and fifth bands, the cirrus index of each pixel is calculated.

[0037] Specifically, the cirrus index can be calculated using the following formula: C = R3 / R5 Where C represents the cirrus index, R3 represents the reflectance value of the third band, and R5 represents the reflectance value of the fifth band.

[0038] In one implementation, for the aforementioned step S103, i.e., when determining the snow-covered area based on the snow index and cirrus index, the following methods can be used: pixels in the remote sensing image whose first snow index is greater than a first threshold, whose second snow index is greater than a second threshold, whose cirrus index is greater than a third threshold, whose reflectance value of the first band is greater than a fourth threshold, and whose reflectance value of the fifth band is less than the fourth threshold are determined as snow-covered areas.

[0039] Specifically, the first threshold can be 0.3, the second threshold can be 0.12, the third threshold can be 2.5, and the fourth threshold can be 0.1. It should be noted that the specific values ​​shown here are all exemplary and are not limited thereto.

[0040] In practice, since snow-covered areas have higher reflectivity in the red band compared to other ground features, when a pixel in a remote sensing image has a reflectivity value R1 > 0.1 in the first band, that pixel can be initially extracted as a candidate pixel for snow-covered areas.

[0041] Meanwhile, compared to other land features, snow-covered areas and water bodies have lower reflectance at 1.6 μm. Therefore, when the reflectance value R5 of a pixel in the remote sensing image is less than 0.1 in the fifth band, some land feature pixels can be further removed from the candidate pixels.

[0042] Furthermore, it is difficult to distinguish between snow-covered areas and cirrus clouds by setting thresholds for reflectance in individual bands. Therefore, in this embodiment of the invention, by analyzing the reflectance spectral characteristics of cirrus clouds and snow-covered areas in various bands of ambient infrared imagery, it was found that the reflectance differences between snow-covered areas and cirrus clouds are significant at channels 1, 3, and 5 (i.e., the first, third, and fifth bands). These differences can be used to construct a snow index to further eliminate some cloud pixels. Based on this, this embodiment of the invention can calculate the first snow index NDSI and the second snow index NDSII based on the reflectance values ​​of the first, third, and fifth bands. Based on a batch of infrared data, the accuracy of the extracted snow pixels is used as the judgment index. When the first snow index NDSI > 0.3 and the second snow index NDSII > 0.12, most of the cirrus cloud pixels in the candidate pixels of the snow-covered area can be eliminated.

[0043] To further eliminate incorrectly extracted cirrus cloud pixels, the spectral characteristics of snow-covered areas and cirrus clouds were analyzed. It was found that the reflectance difference between snow-covered areas and cirrus clouds was small at channels 3 and 5 (i.e., the third and fifth bands). However, the reflectance of the snow-covered area in channel 3 was much greater than that in channel 5. Based on this, in this embodiment of the invention, the cirrus index can be calculated by combining the reflectance of channels 3 and 5 to eliminate cirrus cloud pixels. When the cirrus index C < 2.5, cirrus cloud pixels can be effectively eliminated from the snow-covered pixels selected in the previous step. The third threshold of 2.5 can be a suitable threshold determined by manually judging the accuracy of cirrus pixel elimination in the image data.

[0044] In summary, by comprehensively analyzing the snow accumulation pixel extraction process, a pixel in a remote sensing image is identified as a snow accumulation pixel when it simultaneously meets the following criteria: first snow index NDSI > 0.3, second snow index NDSI > 0.12, cirrus index C > 2.5, reflectance value R5 < 0.1 in the fifth band, and reflectance value R1 > 0.1 in the first band. This pixel belongs to a snow-covered area and is assigned a value of 1. Otherwise, the pixel is determined not to belong to a snow-covered area and is assigned a value of 0. Finally, the pixels assigned a value of 1 are used as the final snow accumulation information extraction result.

[0045] The method provided in this embodiment of the invention, by combining the modified snow cover indexes NDSI and NDSII, as well as the reflectivity value and cirrus index of the specified band combination, can effectively extract snow cover information from the Emergency Disaster Reduction Satellite 2 data by setting a specific threshold; at the same time, it removes easily confused non-snow cover information such as high-level cirrus clouds, greatly improving the accuracy of snow cover extraction.

[0046] For ease of understanding, this embodiment of the invention also provides a specific method for snow extraction based on remote sensing imagery from the infrared camera of the Emergency Disaster Reduction Satellite-2. Taking a single-scene infrared image acquired by the infrared camera of the Emergency Disaster Reduction Satellite-2 as an example, the single-scene image contains five bands from red light to near-infrared. See also... Figure 2 The flowchart shown illustrates a method for snow cover extraction based on remote sensing images from the infrared camera of the Emergency Disaster Reduction Satellite-2, showing that the method mainly includes the following steps S201 to S203: Step S201: Obtain remote sensing images from the infrared camera of the Emergency Disaster Reduction Satellite 2 to extract snow cover information.

[0047] In practice, the input remote sensing image is first radiometrically corrected by combining the calibration coefficients to convert the DN value of the image into a radiometric value. Then, atmospheric correction is performed on the remote sensing image through the atmospheric radiative transfer model to correct the radiometric value of the top atmosphere to the reflectivity value of the surface.

[0048] Step S202: By determining whether each pixel in the remote sensing image meets the modified snow index threshold, and by formulating the band combination and the reflectance threshold of each band, it is determined whether each pixel belongs to the snow distribution range.

[0049] In practice, the first snow index NDSI of each pixel is calculated based on the reflectance values ​​of each pixel in the first and fifth bands. The second snow index NDSII of each pixel is calculated based on the reflectance values ​​of each pixel in the first and fourth bands. The cirrus index C of each pixel is calculated based on the reflectance values ​​of each pixel in the third and fifth bands. Then, it is determined whether the following conditions are met simultaneously for each pixel: first snow index NDSI > 0.3, second snow index NDSII > 0.12, cirrus index C > 2.5, reflectance value R5 < 0.1 in the fifth band, and reflectance value R1 > 0.1 in the first band. If the conditions are met, the pixel is identified as belonging to the snow distribution area and assigned a value of 1; otherwise, the pixel is assigned a value of 0.

[0050] Step S203: Obtain the snow extraction results.

[0051] In practice, pixels with a value of 1 are determined as the final snow extraction result.

[0052] The snow extraction method based on remote sensing images from the infrared camera of the Emergency Disaster Reduction Satellite 2 provided in this embodiment of the invention, combined with the modified snow index NDSI and NDSII, and the reflectance value of a specified band combination, can effectively extract snow information from the Emergency Disaster Reduction Satellite 2 data by setting a specific threshold; at the same time, by using the cirrus index to remove easily confused non-snow information such as high-level cirrus clouds, the accuracy of snow extraction is greatly improved.

[0053] In addition to the aforementioned snow cover information extraction method, this embodiment of the invention also provides a snow cover information extraction device, see [link to device]. Figure 3 The schematic diagram shown illustrates the structure of a snow cover information extraction device, indicating that the device mainly comprises the following parts: The preprocessing module 301 is used to acquire remote sensing images of snow cover information to be extracted and to preprocess the remote sensing images. Calculation module 302 is used to calculate the snow cover index and cirrus index for a specified band of each pixel in the preprocessed remote sensing image; Snow cover area determination module 303 is used to determine the snow cover area based on the snow cover index and cirrus cloud index.

[0054] The snow information extraction device provided in this embodiment of the invention can effectively extract snow information from remote sensing images in Emergency Disaster Reduction Satellite 2 data by using snow index and cirrus cloud index of specified bands. At the same time, it can remove easily confused non-snow information such as high-level cirrus clouds in remote sensing images, thereby improving the accuracy of snow extraction.

[0055] In one embodiment, the preprocessing module 301 is further configured to: correct the remote sensing image based on a predetermined calibration coefficient, converting the DN value of the remote sensing image into a radiative value; and correct the remote sensing image based on an atmospheric radiative transfer model, converting the radiative value into a surface reflectance value.

[0056] In one embodiment, the calculation module 302 is further configured to: extract the reflectance value of each pixel in the remote sensing image in a specified band; wherein the specified band includes: a first band, a third band, a fourth band, and a fifth band.

[0057] In one embodiment, the calculation module 302 is further configured to: calculate a first snow index for each pixel based on the reflectance values ​​of each pixel in the first and fifth bands of the preprocessed remote sensing image; calculate a second snow index for each pixel based on the reflectance values ​​of each pixel in the first and fourth bands of the preprocessed remote sensing image; and calculate a cirrus index for each pixel based on the reflectance values ​​of each pixel in the third and fifth bands of the preprocessed remote sensing image.

[0058] In one embodiment, the calculation module 302 is further configured to: calculate the first snow index according to the following formula: NDSI = (R1 - R5) / (R1 + R5) Wherein, NDSI represents the first snow index, R1 represents the reflectance value of the first band, and R5 represents the reflectance value of the fifth band. The aforementioned calculation module 302 is further configured to: calculate the second snow index according to the following formula: NDSII = (R1 - R4) / (R1 + R4) NDSII represents the second snow index, and R4 represents the reflectance value of the fourth band.

[0059] In one embodiment, the calculation module 302 is further configured to: calculate the cirrus index according to the following formula: C = R3 / R5 Where C represents the cirrus index, R3 represents the reflectance value of the third band, and R5 represents the reflectance value of the fifth band.

[0060] In one embodiment, the snow-covered area determination module 303 is further configured to: determine the pixels in the remote sensing image that have a first snow index greater than a first threshold, a second snow index greater than a second threshold, a cirrus index greater than a third threshold, a reflectance value of a first band greater than a fourth threshold, and a reflectance value of a fifth band less than a fourth threshold as snow-covered areas.

[0061] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0062] This invention also provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0063] Figure 4 The present invention provides a schematic diagram of the structure of an electronic device 100, which includes a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0064] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0065] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0067] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0068] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0069] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of extracting snow information, characterized by, The method comprises the following steps: acquiring remote sensing images to be extracted snow information, and preprocessing the remote sensing images; calculating the snow index and the cirrus index of each pixel of the preprocessed remote sensing images in the specified wave band; determining the snow area based on the snow index and the cirrus index.

2. The method of claim 1, wherein, The preprocessing of the remote sensing images comprises the following steps: correcting the remote sensing images based on the predetermined calibration coefficient, and converting the DN value of the remote sensing images into a radiation value; correcting the remote sensing images based on the atmospheric radiation transfer model, and converting the radiation value into the reflectivity value of the ground surface.

3. The method of claim 2, wherein, Before calculating the snow index and the cirrus index of each pixel of the preprocessed remote sensing images in the specified wave band, the method further comprises the following steps: extracting the reflectivity value of each pixel of the remote sensing images in the specified wave band; wherein the specified wave band comprises a first wave band, a third wave band, a fourth wave band and a fifth wave band.

4. The method of claim 3, wherein, The calculation of the snow index and the cirrus index of each pixel of the preprocessed remote sensing images in the specified wave band comprises the following steps: calculating the first snow index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the first wave band and the fifth wave band; calculating the second snow index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the first wave band and the fourth wave band; calculating the cirrus index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the third wave band and the fifth wave band.

5. The method of claim 4, wherein, The calculation of the first snow index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the first wave band and the fifth wave band comprises the following steps: calculating the first snow index according to the following formula: NDSI= (R1-R5) / (R1+R5) wherein NDSI represents the first snow index, R1 represents the reflectivity value of the first wave band, and R5 represents the reflectivity value of the fifth wave band; The calculation of the second snow index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the first wave band and the fourth wave band comprises the following steps: calculating the second snow index according to the following formula: NDSII= (R1-R4) / (R1+R4) wherein NDSII represents the second snow index, and R4 represents the reflectivity value of the fourth wave band.

6. The method of claim 4, wherein, The calculation of the cirrus index of each pixel based on the reflectivity value of each pixel of the preprocessed remote sensing images in the third wave band and the fifth wave band comprises the following steps: calculating the cirrus index according to the following formula: C=R3 / R5 wherein C represents the cirrus index, R3 represents the reflectivity value of the third wave band, and R5 represents the reflectivity value of the fifth wave band.

7. The method of claim 4, wherein, The determination of the snow area based on the snow index and the cirrus index comprises the following steps: determining the pixels in the remote sensing images as the snow area, wherein the first snow index of the pixels is greater than a first threshold value, the second snow index of the pixels is greater than a second threshold value, the cirrus index of the pixels is greater than a third threshold value, the reflectivity value of the first wave band of the pixels is greater than a fourth threshold value, and the reflectivity value of the fifth wave band of the pixels is less than a fourth threshold value.

8. A snow accumulation information extraction device characterized by comprising: The method comprises the following steps: A preprocessing module is configured to acquire a remote sensing image to be used for extracting snow information, and to perform preprocessing on the remote sensing image. A calculation module is configured to calculate a snow index and a cirrus index of each pixel of a specified wave band of the remote sensing image after preprocessing. A snow region determination module is configured to determine a snow region based on the snow index and the cirrus index.

9. An electronic device, comprising: A computer program product includes a processor and a memory storing computer executable instructions executable by the processor, and the processor executes the computer executable instructions to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program product, when executed by the processor, implements the steps of the method of any one of claims 1 to 7.

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