Method for extracting water body based on multispectral remote sensing data

A remote sensing data and multi-spectral technology, which is applied in the field of multi-spectral remote sensing, can solve the problems of difficult and high-precision water body information extraction and lack of universality, and achieve good applicability, easy promotion and application, and good robustness

Active Publication Date: 2021-04-06
INST OF AGRI RESOURCES & REGIONAL PLANNING CHINESE ACADEMY OF AGRI SCI +2
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AI Technical Summary

Problems solved by technology

However, one of the disadvantages of the object-oriented method is that it is not universal, and it is difficult to achieve high-precision water body information extraction in a large range.

Method used

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  • Method for extracting water body based on multispectral remote sensing data
  • Method for extracting water body based on multispectral remote sensing data
  • Method for extracting water body based on multispectral remote sensing data

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Embodiment Construction

[0028] The present invention will be described in detail below in conjunction with specific embodiments.

[0029] Step 1: Use high-resolution satellites, namely Landsat 8, Sentinel 2, and GF 1, and select a large number of reflectance data for eight surface types, including inland water bodies, ocean water bodies, shadows, snow, vegetation, and dry land , buildings, clouds.

[0030] Step 2: Average the reflectance data of the eight land surface types selected above to obtain the average reflectance of each land type; the average reflectance figure 1 shown.

[0031] Step 3: Divide each band of the average reflectance corresponding to each surface type obtained above by the respective reflectance of the green light band to obtain the corresponding proportional coefficient; the results of the proportional coefficient are shown in the following table:

[0032] Table 1 Scale factor results

[0033]

[0034]

[0035] Step 4: Combining the proportional coefficients in Table 1 ...

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Abstract

The invention discloses a method for extracting a water body based on multispectral remote sensing data. The method comprises the following steps: 1, acquiring a large amount of remote sensing reflectivity data of various earth surface types as a training data set; 2, respectively solving the average reflectivity of each ground class training data set; 3, dividing each wave band of each type of average reflectivity by respective green light wave band reflectivity to obtain a corresponding proportionality coefficient; 4, combining the proportionality coefficient with a water body general model, and listing an inequality group; and 5, solving the inequality group to obtain a final water body extraction model. Domestic satellite data can be used for extracting the water body, and the problem that ice and snow are difficult to distinguish from the water body is solved.

Description

technical field [0001] The invention belongs to the technical field of multispectral remote sensing, in particular to a method for extracting water bodies based on multispectral remote sensing data. Background technique [0002] The impact of surface water change on ecology, society and economy is a hotspot of current research. Optical remote sensing water resources research can be mainly divided into: research on climate and water resources, extraction of different types of water bodies, flood monitoring, research on river morphology, long-term series research on water bodies, and research on coastal zone changes. Currently, Landsat imagery is the most widely used data source for change analysis, identification and detection of surface water. Most of the studies on water body extraction based on Landsat remote sensing images focus on the extraction of water bodies such as rivers and lakes in large areas. In recent years, some scholars have used Sentinel 2 images to extrac...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/20G01N21/25
CPCG01N21/25G06V10/143G06F18/214
Inventor 高懋芳邓开元刘三超任超李召良李石磊
Owner INST OF AGRI RESOURCES & REGIONAL PLANNING CHINESE ACADEMY OF AGRI SCI
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