Improved linear spectral mixture model based vegetation coverage estimation method

Abstract

The invention discloses an improved linear spectral mixture model based vegetation coverage estimation method. The method includes: acquiring image data in a study area; subjecting the acquired image data to geometric coarse correction and radiation correction preprocessing; adopting a pixel purity index method for extracting end members from an image obtained by preprocessing, and constructing a variable-end-member linear spectral decomposition model; and extracting vegetation coverage information in the study area according to the constructed variable-end-member linear spectral decomposition model. By application of the method, vegetation coverage estimation accuracy can be improved.

Description

technical field [0001] The invention relates to vegetation coverage estimation technology, in particular to a vegetation coverage estimation method based on an improved linear spectrum mixed model. Background technique [0002] Vegetation is a comprehensive product of the long-term interaction of landform, hydrology, soil, climate change and human activities, and its distribution, composition and development are closely related to environmental conditions, especially climatic conditions. Vegetation coverage refers to the percentage of the vertical projection of vegetation (branches, stems, leaves) on the ground to the statistical area of ​​the surface. It is an important parameter to describe the horizontal coverage of surface vegetation and an important quantitative information to measure the coverage of surface vegetation. , plays an important role in assessing the degree of land degradation and desertification. At the same time, vegetation coverage is also an important c...

AI Technical Summary

Problems solved by technology

But in fact, in images with low spatial resolution and medium spatial resolution, most of the mixed pixels are only composed of a few of the endmembers of the entire image, and the method of decomposing the endmembers of the entire image makes it difficult to estimate the vegetation coverage. The degree process is very complex and the amount of calculation is large

[0007] In order to simplify the process of estimating vegetation coverage and the amount of computation, the existing technology proposes a Linear Spectral Mixture Model (LSMM, Linear Spectral Mixture Model), which calculates the response value between the actual spectrum of the pixel and the reference endmember spectrum to obtain Judge the degree of similarity between two spectra, and select the reference endmember spectrum with high similarity with the pixel spectrum, so as to dynamically determine the number of reference endmembers participating in spectral decomposition, so that it is not necessary to use all image endmembers for decomposition, thereby simplifying the process and However, this method judges the degree of similarity by calculating the response value between the actual spectrum of the pixel and the reference endmember spectrum, and determines the number of reference endmembers participating in the spectral decomposition according to the similarity, so that the estimation accuracy of vegetation coverage is not high.

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