A method and system for identifying abnormal water color areas based on MSI images

Abstract

The present invention discloses a method and system for identifying abnormal water color areas based on MSI images, the method comprising: calculating an MCI index based on MSI image data; binarizing the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0; determining the portion assigned with a value of 1 as a first abnormal water color area; and correcting the boundary of the first abnormal water color area to obtain a second abnormal water color area. The present invention can effectively solve the problem of low revisit period of existing images based on MSI images, thereby effectively improving the recognition effect of abnormal water color areas.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for identifying abnormal water color areas based on MSI (Multispectral Instrument, optical sensor multispectral imager) images. Background Art

[0002] At present, when monitoring rivers, lakes and reservoirs, most of them use domestic "Gaofen series" satellite remote sensing data to extract abnormal water color and black water areas. However, due to the low revisit period of "Gaofen series" satellites, there is not much effective data, and there are only four spectral bands. The band setting is not suitable for water color remote sensing, so the effect is poor in identifying abnormal water color areas.

[0003] With the successful launch of the Sentinel-2 satellite, the MSI it carries has the characteristics of multi-spectral, wide range, and high revisit period. The networking of Sentinel-2A and 2B satellites can achieve a 5-day revisit period; MSI has different spatial resolutions of 10 / 20 / 60m from visible light (VIS), near infrared (NIR) to short-wave infrared (SWIR), and has 13 bands. It is the only sensor in the world's orbiting remote sensing satellite with a ground resolution of up to 10m and three special bands (B5, B6, B7) designed to obtain spectral characteristic information of vegetation in the near-infrared "red edge" (Red edge, 690~800nm) range, providing a new data source for water environment monitoring. However, this data is rarely reported in the application of remote sensing monitoring of water color anomalies. Summary of the invention

[0004] In view of this, the present invention provides a method for identifying abnormal water color areas based on MSI images, which can effectively solve the problem of low revisit period of existing images, and thus can effectively improve the recognition effect of abnormal water color areas.

[0005] The present invention provides a method for identifying abnormal water color areas based on MSI images, comprising:

[0006] The MCI index was calculated based on the MSI imaging data;

[0007] Binarize the MCI index, assign a value less than 0 to 1, and assign a value greater than or equal to 0 to 0;

[0008] The portion assigned a value of 1 is determined as the first water color abnormal area;

[0009] The boundary of the first abnormal water color region is corrected to obtain a second abnormal water color region.

[0010] Preferably, the MCI index is calculated based on the MSI image data, including:

[0011] Resample the MSI image data;

[0012] The resampled MSI image data is converted from the apparent reflectance to the atmospheric bottom reflectance;

[0013] The MCI index is calculated based on the atmospheric bottom reflectivity.

[0014] Preferably, the boundary of the first abnormal water color region is corrected to obtain the second abnormal water color region, including:

[0015] The boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the second abnormal water color area.

[0016] Preferably, the conversion of the resampled MSI image data from the apparent reflectance to the atmospheric bottom reflectance includes one of the following implementations:

[0017] The resampled MSI image data is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the dedicated atmospheric correction module SEN2COR;

[0018] The resampled MSI image data is converted from apparent reflectance to atmospheric bottom reflectance based on 6S software.

[0019] Preferably, the calculating of the MCI index based on the atmospheric bottom reflectivity comprises:

[0020] Based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index, where r BOA is the atmospheric bottom reflectivity.

[0021] A water color abnormal area recognition system based on MSI images, comprising:

[0022] A calculation module, used for calculating the MCI index based on the MSI image data;

[0023] A processing module, used for performing binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0024] A determination module, used for determining the portion assigned with a value of 1 as a first water color abnormal area;

[0025] The correction module is used to correct the boundary of the first abnormal water color area to obtain a second abnormal water color area.

[0026] Preferably, the calculation module includes:

[0027] A resampling unit, used for resampling the MSI image data;

[0028] A conversion unit is used to convert the resampled MSI image data from the apparent reflectance to the atmospheric bottom reflectance;

[0029] A calculation unit is used to calculate the MCI index based on the atmospheric bottom reflectivity.

[0030] Preferably, the correction module is specifically used for:

[0031] The boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the second abnormal water color area.

[0032] Preferably, the conversion unit is specifically used to perform one of the following methods:

[0033] The resampled MSI image data is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the dedicated atmospheric correction module SEN2COR;

[0034] The resampled MSI image data is converted from apparent reflectance to atmospheric bottom reflectance based on 6S software.

[0035] Preferably, the computing unit is specifically used for:

[0036] Based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index, where r BOA is the atmospheric bottom reflectivity.

[0037] In summary, the present invention discloses a method for identifying abnormal water color areas based on MSI images. When it is necessary to identify abnormal water color areas, the MCI index is first calculated based on the MSI image data, and then the MCI index is binarized, and values ​​less than 0 are assigned to 1, and values ​​greater than or equal to 0 are assigned to 0; the portion assigned to 1 is determined as the first abnormal water color area; the boundary of the first abnormal water color area is corrected to obtain the second abnormal water color area. The present invention can effectively solve the problem of low revisit period of existing images based on MSI images, and thus can effectively improve the recognition effect of abnormal water color areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A method flow chart of Embodiment 1 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention;

[0040] Figure 2 It is a method flow chart of Embodiment 2 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention;

[0041] Figure 3 A method flow chart of Embodiment 3 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention;

[0042] Figure 4 It is a structural schematic diagram of Embodiment 1 of a system for identifying abnormal water color areas based on MSI images disclosed in the present invention;

[0043] Figure 5 It is a structural schematic diagram of Embodiment 2 of a water color abnormal area recognition system based on MSI images disclosed in the present invention;

[0044] Figure 6 This is a structural schematic diagram of Example 3 of a water color abnormal area recognition system based on MSI images disclosed in the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, it is a method flow chart of Embodiment 1 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention. The method may include the following steps:

[0047] S101, calculating the MCI index based on the MSI image data;

[0048] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0049] S102, performing binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0050] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0051] S103, determining the portion assigned a value of 1 as the first water color abnormal area;

[0052] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0053] S104, correcting the boundary of the first abnormal water color region to obtain a second abnormal water color region.

[0054] Then, the determined first abnormal water color region boundary is corrected to obtain a final abnormal water color region.

[0055] In summary, in the above embodiment, when it is necessary to identify the abnormal water color area, the MCI index is first calculated based on the MSI image data, and then the MCI index is binarized, and the value less than 0 is assigned to 1, and the value greater than or equal to 0 is assigned to 0; the part assigned to 1 is determined as the first abnormal water color area; the boundary of the first abnormal water color area is corrected to obtain the second abnormal water color area. Based on the MSI image, the problem of low revisit cycle of existing images can be effectively solved, and the recognition effect of abnormal water color areas can be effectively improved.

[0056] like Figure 2 As shown, it is a method flow chart of Embodiment 2 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention. The method may include the following steps:

[0057] S201, calculating the MCI index based on the MSI image data;

[0058] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0059] S202, performing binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0060] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0061] S203, determining the portion assigned a value of 1 as the first water color abnormal area;

[0062] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0063] S204. Correct the boundary of the first abnormal water color region in combination with visual interpretation to obtain a second abnormal water color region.

[0064] Then, the determined boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the final abnormal water color area.

[0065] In summary, based on the above embodiments, this embodiment can correct the boundary of the first abnormal water color area by combining visual interpretation.

[0066] like Figure 3 As shown, it is a method flow chart of Embodiment 3 of a method for identifying abnormal water color areas based on MSI images disclosed in the present invention. The method may include the following steps:

[0067] S301, resampling the MSI image data;

[0068] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0069] Specifically, the MSI image data can be resampled using SNAP software so that all bands are sampled to 10 meters.

[0070] S302, converting the resampled MSI image data from the surface reflectance to the atmospheric bottom reflectance;

[0071] This can be achieved in one of the following ways:

[0072] (1) The resampled MSI image data is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the atmospheric correction module SEN2COR;

[0073] (2) The resampled MSI image data is converted from surface reflectance to bottom atmospheric reflectance based on 6S software;

[0074] (3) The resampled MSI image data is converted from surface reflectance to bottom atmospheric reflectance using other software.

[0075] S303, calculating the MCI index based on the atmospheric bottom reflectivity;

[0076] Then, based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index. BOA It is the atmospheric bottom reflectivity. For the band setting of MSI, the atmospheric bottom reflectivity of B4 at 665nm, B6 at 740nm and B5 at 705nm is adopted.

[0077] S304, binarizing the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0078] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0079] S305, determining the portion assigned a value of 1 as the first water color abnormal area;

[0080] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0081] S306. Correct the boundary of the first abnormal water color area in combination with visual interpretation to obtain a second abnormal water color area.

[0082] Then, the determined boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the final abnormal water color area.

[0083] In summary, the present invention can effectively solve the problem of low revisit period of existing images based on MSI images, and thus can effectively improve the recognition effect of abnormal water color areas.

[0084] like Figure 4 FIG. 1 is a schematic diagram of a structure of a system for identifying abnormal water color areas based on MSI images according to an embodiment of the present invention. The system may include:

[0085] A calculation module 401 is used to calculate the MCI index based on the MSI image data;

[0086] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0087] A processing module 402 is used to perform binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0088] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0089] A determination module 403, configured to determine the portion assigned a value of 1 as a first water color abnormal area;

[0090] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0091] The correction module 404 is used to correct the boundary of the first abnormal water color region to obtain a second abnormal water color region.

[0092] Then, the determined first abnormal water color region boundary is corrected to obtain a final abnormal water color region.

[0093] In summary, in the above embodiment, when it is necessary to identify the abnormal water color area, the MCI index is first calculated based on the MSI image data, and then the MCI index is binarized, and the value less than 0 is assigned to 1, and the value greater than or equal to 0 is assigned to 0; the part assigned to 1 is determined as the first abnormal water color area; the boundary of the first abnormal water color area is corrected to obtain the second abnormal water color area. Based on the MSI image, the problem of low revisit cycle of existing images can be effectively solved, and the recognition effect of abnormal water color areas can be effectively improved.

[0094] like Figure 5 FIG. 1 is a schematic diagram of a structure of a water color abnormal area recognition system based on MSI images according to an embodiment 2 of the present invention. The system may include:

[0095] A calculation module 501 is used to calculate the MCI index based on the MSI image data;

[0096] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0097] A processing module 502 is used to perform binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0098] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0099] A determination module 503, configured to determine the portion assigned a value of 1 as a first water color abnormal area;

[0100] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0101] The correction module 504 is used to correct the boundary of the first abnormal water color area in combination with visual interpretation to obtain a second abnormal water color area.

[0102] Then, the boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the final abnormal water color area.

[0103] In summary, based on the above embodiments, this embodiment can correct the boundary of the first abnormal water color area by combining visual interpretation.

[0104] like Figure 6 FIG. 1 is a schematic diagram of a structure of a water color abnormal area recognition system based on MSI images according to Embodiment 3 of the present invention. The system may include:

[0105] A resampling unit 601, used for resampling the MSI image data;

[0106] When it is necessary to identify abnormal water color areas, the MCI index is first calculated using the MSI image data. The MSI sensor carried by the Sentinel-2 satellite has a high repetition period and can basically cover once every 5 days, so the MSI image data has great utilization value. When calculating the MCI index based on the MSI image data, it can be completed through professional software such as ENVI and SNAP.

[0107] Specifically, the SNAP software can be used to resample the Sentinel-2 data so that all bands are sampled to 10 meters.

[0108] The conversion unit 602 is used to convert the resampled MSI image data from the apparent reflectance to the atmospheric bottom reflectance;

[0109] This can be achieved in one of the following ways:

[0110] (1) The resampled MSI image data is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the atmospheric correction module SEN2COR;

[0111] (2) The resampled MSI image data is converted from surface reflectance to bottom atmospheric reflectance based on 6S software;

[0112] (3) The resampled MSI image data is converted from surface reflectance to bottom atmospheric reflectance using other software.

[0113] A calculation unit 603 is used to calculate the MCI index based on the atmospheric bottom reflectivity;

[0114] Then, based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index. BOAIt is the atmospheric bottom reflectivity. For the band setting of MSI, the atmospheric bottom reflectivity of B4 at 665nm, B6 at 740nm and B5 at 705nm is adopted.

[0115] A processing module 604 is used to perform binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0;

[0116] After the MCI index is calculated, the calculated MCI index is further binarized, and values ​​less than 0 in the binarization result are assigned a value of 1, and values ​​greater than or equal to 0 in the binarization result are assigned a value of 0.

[0117] A determination module 605 is used to determine the portion assigned with a value of 1 as a first water color abnormal area;

[0118] Then, the portion assigned with a value of 1 is determined as the first abnormal water color region, that is, the portion assigned with a value of 1 is determined as the preliminary abnormal water color region.

[0119] The correction module 606 is used to correct the boundary of the first abnormal water color area in combination with visual interpretation to obtain a second abnormal water color area.

[0120] Then, the determined boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the final abnormal water color area.

[0121] In summary, the present invention can effectively solve the problem of low revisit period of existing images based on MSI images, and thus can effectively improve the recognition effect of abnormal water color areas.

[0122] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0123] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may 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.

[0124] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0125] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying abnormal water color areas based on MSI images, characterized in that: include: Resample the MSI image data; The resampled MSI image is converted from the apparent reflectance to the atmospheric bottom reflectance, and the MCI index is calculated based on the atmospheric bottom reflectance; Binarize the MCI index, assign a value less than 0 to 1, and assign a value greater than or equal to 0 to 0; The portion assigned a value of 1 is determined as the first water color abnormal area; Correcting the boundary of the first abnormal water color region to obtain a second abnormal water color region; The calculating of the MCI index based on the atmospheric bottom reflectivity includes: Based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index, where r BOA is the atmospheric bottom reflectivity; Correcting the boundary of the first abnormal water color region to obtain a second abnormal water color region includes: The boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the second abnormal water color area.

2. The method according to claim 1, characterized in that The conversion of the resampled MSI image from the apparent reflectance to the atmospheric bottom reflectance includes one of the following implementation methods: The resampled MSI image is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the dedicated atmospheric correction module SEN2COR; The resampled MSI image is converted from apparent reflectance to atmospheric bottom reflectance based on 6S software.

3. A water color abnormal area recognition system based on MSI images, characterized in that: include: A calculation module, used for resampling the MSI image data; The resampled MSI image is converted from the apparent reflectance to the atmospheric bottom reflectance, and the MCI index is calculated based on the atmospheric bottom reflectance; A processing module, used for performing binarization processing on the MCI index, assigning a value less than 0 to 1, and assigning a value greater than or equal to 0 to 0; A determination module, used for determining the portion assigned with a value of 1 as a first water color abnormal area; A correction module, used for correcting the boundary of the first abnormal water color area to obtain a second abnormal water color area; The calculation module is specifically used for: Based on the formula MCI = r BOA (B5)-1.005*(r BOA (B4)+(r BOA (B6)-r BOA (B4)) (705-665) / (740-665))) calculates the MCI index, where r BOA is the atmospheric bottom reflectivity; The correction module is specifically used for: The boundary of the first abnormal water color area is corrected in combination with visual interpretation to obtain the second abnormal water color area.

4. The system according to claim 3, characterized in that The calculation module is specifically used to perform one of the following methods: The resampled MSI image is converted from the apparent reflectance to the bottom reflectance of the atmosphere based on the dedicated atmospheric correction module SEN2COR; The resampled MSI image is converted from apparent reflectance to atmospheric bottom reflectance based on 6S software.

Images