CN108986116B - Mangrove forest extraction method and system based on remote sensing image - Google Patents
Mangrove forest extraction method and system based on remote sensing image Download PDFInfo
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- CN108986116B CN108986116B CN201810775694.4A CN201810775694A CN108986116B CN 108986116 B CN108986116 B CN 108986116B CN 201810775694 A CN201810775694 A CN 201810775694A CN 108986116 B CN108986116 B CN 108986116B
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- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06T2207/10032—Satellite or aerial image; Remote sensing
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Abstract
The invention discloses a mangrove forest extraction method and system based on remote sensing images. The method comprises the following steps: obtaining a remote sensing image of a region to be researched; identifying a water body area of an area to be researched by using a remote sensing image; expanding a certain distance to the outer side of each water body area by taking the edge of each water body area as a starting point, and determining a water and land boundary area; performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image; segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images; dividing the vegetation type and the non-vegetation type of the plurality of divided areas, removing the areas belonging to the non-vegetation type, and obtaining a plurality of vegetation area images; and classifying the vegetation area images into mangrove forest images and non-mangrove forest images by using the texture features and the topological features of the mangrove forest. The method and the system can improve the accuracy of spatial extraction of mangrove forest.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a mangrove forest extraction method and system based on remote sensing images.
Background
The mangrove forest refers to a tidal flat wetland woody biocenosis composed of evergreen shrubs or arbors which grow on the upper part of the intertidal zone of the tropical and subtropical low-energy coast and are soaked by periodic tidal water and take mangrove plants as main bodies. The species of the composition comprises herbaceous and liana mangrove. The mangrove forest grows on the beach shoal of land and sea boundary zone, and is a special ecological system of land to sea transition. Because the mangrove forest has the functions of preventing wind and protecting dike, stabilizing coastline, purifying water quality and the like, the research on the distribution of the mangrove forest has great significance for effectively strengthening the wetland protection management and ecological restoration of the mangrove forest.
The traditional mangrove spatial extraction method only utilizes spectral information and cannot accurately extract mangrove spatial information.
Disclosure of Invention
The invention aims to provide a mangrove forest extraction method and system based on remote sensing images, which can improve the accuracy of mangrove forest spatial extraction.
In order to achieve the purpose, the invention provides the following scheme:
a mangrove forest extraction method based on remote sensing images comprises the following steps:
obtaining a remote sensing image of a region to be researched;
identifying a water body area of the area to be researched by using the remote sensing image;
expanding a certain distance to the outer side of each water body area by taking the edge of each water body area as a starting point, and determining a water and land boundary area;
performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image;
segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images;
dividing the vegetation type and the non-vegetation type of the plurality of the divided areas, removing the areas belonging to the non-vegetation type, and obtaining a plurality of vegetation area images;
and classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI arithmetic characteristics, LSWI arithmetic characteristics, texture characteristics and topological characteristics of the mangrove forest.
Optionally, the identifying the water body region of the region to be researched by using the remote sensing image specifically includes:
performing band operation on the remote sensing image by using an MNDWI algorithm to obtain an MNDWI image;
calculating an optimal threshold value for dividing the water body and the non-water body by adopting an Otsu algorithm;
and carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
Optionally, the segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images specifically includes:
and dividing adjacent pixels with the similarity of texture, brightness and color within a preset range into the same region.
The invention also discloses a mangrove forest extraction system based on the remote sensing image, which comprises the following steps:
the acquisition module is used for acquiring a remote sensing image of a region to be researched;
the water body identification module is used for identifying the water body area of the area to be researched by utilizing the remote sensing image;
the region expansion module is used for expanding a certain distance to the outer side of each water body region by taking the edge of each water body region as a starting point to determine an land and water boundary region;
the false color synthesis module is used for performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image;
the segmentation module is used for segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images;
the vegetation type screening module is used for dividing the vegetation types and the non-vegetation types of the plurality of the divided areas, removing the areas belonging to the non-vegetation types and obtaining a plurality of vegetation area images;
and the mangrove forest identification module is used for classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI (normalized difference of absolute difference.
Optionally, the water body identification module specifically includes:
the band operation unit is used for performing band operation on the remote sensing image by utilizing an MNDWI algorithm to obtain an MNDWI image;
the threshold determining unit is used for calculating the optimal threshold for dividing the water body and the non-water body by adopting an Otsu algorithm;
and the binarization unit is used for carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
Optionally, the segmentation module specifically includes:
and the similar pixel dividing unit is used for dividing adjacent pixels of which the similarity of textures, brightness and colors is within a preset range into the same region.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the mangrove forest extraction method and system based on the remote sensing image determine the land-water junction area by identifying the water body, then combine the pseudo-color synthesis technology and the similar pixel segmentation to remove the non-vegetation area, and finally identify the mangrove forest from the vegetation area by utilizing the characteristics of the mangrove forest. The invention combines the water body identification, the region external expansion, the false color synthesis technology, the similar pixel segmentation technology and other technologies to extract the mangrove forest, thereby improving the accuracy of mangrove forest space extraction.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of the method of the present invention for extracting mangrove forest based on remote sensing image;
FIG. 2 is a system structure diagram of the remote sensing image-based mangrove forest extraction system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a mangrove forest extraction method and system based on remote sensing images, which can improve the accuracy of mangrove forest spatial extraction.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a flow chart of the method for extracting mangrove forest based on remote sensing image according to the present invention.
Referring to fig. 1, the mangrove forest extraction method based on remote sensing images comprises the following steps:
step 101: and acquiring a remote sensing image of the area to be researched. The remote sensing image is a Landsat remote sensing image.
Step 102: identifying a water body area of the area to be researched by using the remote sensing image; the step 102 specifically includes:
because the Landsat remote sensing image is a multiband image, the MNDWI algorithm is utilized to perform band operation on the remote sensing image to obtain an MNDWI image; calculating an optimal threshold value for dividing the water body and the non-water body by adopting an Otsu algorithm; and carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
In the above steps, the mathematical expression of the MNDWI algorithm is:
MNDWI＝(Green-MIR)/(Green+MIR)
wherein Green is Green light wave band, MIR is middle infrared wave band.
Step 103: and (4) expanding a certain distance to the outer side of each water body area by taking the edge of each water body area as a starting point, and determining the land and water boundary area. The determination process of the extended distance comprises the following steps: firstly, acquiring the average width of regions such as sand beach, wetland, intertidal zone and the like which are required to pass from a water body to a continent in the existing data from the water body to the continent; determining a remote sensing distance between equipment for acquiring a remote sensing image and an actual position of the remote sensing image on the earth; and determining the distance of the average width on the remote sensing image according to the remote sensing distance so as to obtain the extended distance.
Step 104: and carrying out false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image. False color synthesis techniques can enhance the resolution of the image.
Step 105: and segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images. The method specifically comprises the following steps: and dividing adjacent pixels with the similarity of texture, brightness and color within a preset range into the same region.
Step 106: and dividing the vegetation type and the non-vegetation type of the plurality of the divided areas, removing the areas belonging to the non-vegetation type, and obtaining a plurality of vegetation area images.
Step 107: and classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI arithmetic characteristics, LSWI arithmetic characteristics, texture characteristics and topological characteristics of the mangrove forest.
FIG. 2 is a system configuration diagram of the remote sensing image-based mangrove forest extraction system according to the embodiment of the present invention.
Referring to fig. 2, the remote sensing image-based mangrove forest extraction system comprises:
the acquisition module 201 is configured to acquire a remote sensing image of a region to be studied.
And the water body identification module 202 is used for identifying the water body area of the area to be researched by utilizing the remote sensing image. The water body identification module 202 specifically comprises a band operation unit, a threshold determination unit and a binarization unit, wherein the band operation unit is used for performing band operation on the remote sensing image by using an MNDWI algorithm to obtain an MNDWI image; the threshold determining unit is used for calculating the optimal threshold for dividing the water body and the non-water body by adopting an Otsu algorithm; and the binarization unit is used for carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
And the region expansion module 203 is used for expanding a certain distance to the outer side of each water body region by taking the edge of each water body region as a starting point to determine the land and water boundary region.
And the false color synthesis module 204 is used for performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image.
And the segmentation module 205 is configured to segment the false color image according to the pixel similarity to obtain a plurality of segmented images. The segmentation module 205 includes: and a similar pixel dividing unit. The similar pixel division unit is used for dividing adjacent pixels of which the similarity of textures, brightness and colors is within a preset range into the same region.
And the vegetation type screening module 206 is configured to divide the vegetation types and the non-vegetation types of the multiple divided areas, remove areas belonging to the non-vegetation types, and obtain multiple vegetation area images.
And the mangrove forest identification module 207 is used for classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI (normalized difference of absolute difference.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the mangrove forest extraction method and system based on the remote sensing image determine the land-water junction area by identifying the water body, then combine the pseudo-color synthesis technology and the similar pixel segmentation to remove the non-vegetation area, and finally identify the mangrove forest from the vegetation area by utilizing the characteristics of the mangrove forest. The invention combines the water body identification, the region external expansion, the false color synthesis technology, the similar pixel segmentation technology and other technologies to extract the mangrove forest, thereby improving the accuracy of mangrove forest space extraction.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. A mangrove forest extraction method based on remote sensing images is characterized by comprising the following steps:
obtaining a remote sensing image of a region to be researched;
identifying a water body area of the area to be researched by using the remote sensing image;
expanding a certain distance to the outer side of each water body area by taking the edge of each water body area as a starting point, and determining a water and land boundary area; the determination of the spread distance includes: acquiring the average width of a beach, a wetland and an intertidal zone which are required to pass from a water body area to a continent in the direction from the water body area to the continent; determining a remote sensing distance between equipment for acquiring a remote sensing image and an actual position of the remote sensing image on the earth; determining the distance of the average width on the remote sensing image according to the remote sensing distance to obtain an extended distance;
performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image;
segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images;
dividing the vegetation type and the non-vegetation type of the plurality of the divided areas, removing the areas belonging to the non-vegetation type, and obtaining a plurality of vegetation area images;
and classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI arithmetic characteristics, LSWI arithmetic characteristics, texture characteristics and topological characteristics of the mangrove forest.
2. The remote sensing image-based mangrove forest extraction method according to claim 1, wherein the identification of the water body region of the region to be studied by using the remote sensing image specifically comprises:
performing band operation on the remote sensing image by using an MNDWI algorithm to obtain an MNDWI image;
calculating an optimal threshold value for dividing the water body and the non-water body by adopting an Otsu algorithm;
and carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
3. The remote sensing image-based mangrove forest extraction method according to claim 1, wherein the segmenting the false color image according to pixel similarity to obtain a plurality of segmented images specifically comprises:
and dividing adjacent pixels with the similarity of texture, brightness and color within a preset range into the same region.
4. The utility model provides a mangrove forest extraction system based on remote sensing image which characterized in that includes:
the acquisition module is used for acquiring a remote sensing image of a region to be researched;
the water body identification module is used for identifying the water body area of the area to be researched by utilizing the remote sensing image;
the region expansion module is used for expanding a certain distance to the outer side of each water body region by taking the edge of each water body region as a starting point to determine an land and water boundary region; the determination of the spread distance includes: acquiring the average width of a beach, a wetland and an intertidal zone which are required to pass from a water body area to a continent in the direction from the water body area to the continent; determining a remote sensing distance between equipment for acquiring a remote sensing image and an actual position of the remote sensing image on the earth; determining the distance of the average width on the remote sensing image according to the remote sensing distance to obtain an extended distance;
the false color synthesis module is used for performing false color synthesis on the land and water boundary area by using a false color synthesis technology to obtain a land and water boundary false color image;
the segmentation module is used for segmenting the false color image according to the pixel similarity to obtain a plurality of segmented images;
the vegetation type screening module is used for dividing the vegetation types and the non-vegetation types of the plurality of the divided areas, removing the areas belonging to the non-vegetation types and obtaining a plurality of vegetation area images;
and the mangrove forest identification module is used for classifying the vegetation area images into mangrove forest images and non-mangrove forest images by utilizing NDVI (normalized difference of absolute difference.
5. The remote sensing image-based mangrove forest extraction system according to claim 4, wherein the water body identification module specifically comprises:
the band operation unit is used for performing band operation on the remote sensing image by utilizing an MNDWI algorithm to obtain an MNDWI image;
the threshold determining unit is used for calculating the optimal threshold for dividing the water body and the non-water body by adopting an Otsu algorithm;
and the binarization unit is used for carrying out binarization processing on the MNDWI image by using the optimal threshold value to obtain a water body area and a non-water body area.
6. The remote sensing image-based mangrove forest extraction system according to claim 4, wherein the segmentation module specifically comprises:
and the similar pixel dividing unit is used for dividing adjacent pixels of which the similarity of textures, brightness and colors is within a preset range into the same region.
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| CN110765941B (en) * | 2019-10-23 | 2022-04-26 | 北京建筑大学 | Seawater pollution area identification method and equipment based on high-resolution remote sensing image |
| CN111767813B (en) * | 2020-06-19 | 2021-09-28 | 宁波大学 | Multi-feature and long-time-sequence probability synergistic mangrove forest extraction method |
| CN112686995B (en) * | 2020-12-25 | 2023-09-12 | 浙江弄潮儿智慧科技有限公司 | Mangrove intelligent supervision system |
| CN113033474A (en) * | 2021-04-14 | 2021-06-25 | 海南大学 | Mangrove forest resource remote sensing interpretation method based on fusion algorithm and model |
| CN113468951A (en) * | 2021-05-20 | 2021-10-01 | 华东师范大学 | Method for detecting mangrove landform based on Landsat satellite remote sensing |
| CN113850706A (en) * | 2021-11-30 | 2021-12-28 | 阿里云计算有限公司 | Regional carbon measuring and calculating method, display platform, cloud server and storage medium |
| CN117011555B (en) * | 2023-10-07 | 2023-12-01 | 广东海洋大学 | Mangrove forest ecological detection method based on remote sensing image recognition |
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