Hyperspectral mixed pixel classification method based on neighbor cooperation enhancement

Abstract

The invention provides a hyperspectral mixed pixel classification method based on neighbor cooperation enhancement, and the method comprises the steps: calculating a spectrum signature matrix of a plurality of target ground features through marked sample ground features; designing a multi-class classifier based on spectrum characteristics, and carrying out the classification of the ground features; carrying out the fusion of spatial structure features in a classification result, and extracting neighbor pixels; carrying out the class marking of unmarked hyperspectral ground features through the neighbor pixels; carrying out the classification and marking of the unmarked hyperspectral ground features through an interaction method; carrying out the further fusion of the spatial features of the target ground features in a mode of neighbor expansion, and completing the final classification and marking. According to the invention, the multi-class classifier is used for the simultaneous classification of ground features, and a problem that a conventional classification method cannot carry out the classification of background features is solved. Moreover, a mode of neighbor cooperation enhancement is employed for marking the unmarked ground objects step by step, thereby achieving the effective fusion of the spectrum features and spatial features of the ground features. The classification effect is good.

Description

technical field [0001] The technical field of hyperspectral image classification of the present invention, in particular, relates to a hyperspectral mixed pixel classification method based on neighbor collaborative enhancement. Background technique [0002] Hyperspectral image classification is an important application in hyperspectral image processing, and its ultimate goal is to classify each pixel in the image. Hyperspectral remote sensing technology uses more spectral bands to make it have a huge advantage in the classification of ground object categories, but the accuracy of ground object spectral information also makes interference and background parts have a certain influence in hyperspectral classification; on the other hand , because the hyperspectral data has the characteristics of large amount of high-dimensional data and small training samples, the Hughes phenomenon is easy to occur during classification. [0003] In recent years, attention has been paid to the ...

AI Technical Summary

Problems solved by technology

At present, most applications use support vector machines and other methods for one-to-one or one-to-many classification; one of the biggest problems with this type of method is that it cannot classify the background part of the hyperspectral image, so when evaluating classification methods, usually They all use the method of removing the background, which leads to the lack of accuracy of this type of method; in addition, this method almost uses pure elements for classification, which leads to the limitation of the classification method and lack of versatility

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