Remote sensing image classification method and system based on neighbor regular joint sparse representation

Abstract

The invention relates to a remote sensing image classification method and a system based on neighbor regular joint sparse representation. The method comprises steps: a to-be-classified remote sensing image is inputted; training samples and testing samples are divided; a data dictionary is built; a regular joint sparse representation model including a neighborhood pixel weight matrix is built, and a joint sparse representation coefficient matrix for each testing sample and the neighborhood pixel weight matrix are optimized in a joint mode; and according to the data dictionary and the optimal joint sparse representation coefficient matrix for the testing sample and the optimal neighborhood pixel weight matrix, the testing sample is classified. While the joint sparse representation coefficient matrix is optimized, the neighborhood pixel weight matrix is also optimized, the neighborhood pixel weight matrix can reflect a similarity relation and a joint sparse representation error relation between neighborhood pixels, the joint sparse representation coefficient can reflect an approximation relation between the testing sample and the data dictionary more accurately, and an accurate and reliable classification result can be acquired.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a classification method and system for remote sensing images based on the joint sparse representation of neighbor regularization. Background technique [0002] Hyperspectral image classification has always been a research hotspot in the field of remote sensing image processing, and it is widely used in crop analysis, military target recognition, geography and geology and other fields. In recent years, sparse representation classification has been successfully introduced into the field of hyperspectral remote sensing image processing. [0003] Using the similarity of pixels in the neighborhood of hyperspectral remote sensing images, Y.Chen et al. proposed a joint sparse representation classification algorithm. The algorithm assumes that the test sample and its neighborhood pixels have the same sparse structure, derives a joint sparse representation model, ...

AI Technical Summary

Problems solved by technology

Due to the complex structure of hyperspectral remote sensing images, there are often noise or background interference pixels in the neighborhood, and some neighborhood pixels are located at the target boundary. At this time, using all the neighborhood pixels indiscriminately will lead to certain deviations in the classification results.

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