Hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding

Abstract

The invention discloses a hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding, and irons out the defects that the prior art cannot makes the most of the spatial information of a hyperspectral image to carry out classification and is low in classification speed. The method comprises the steps: (1) inputting a to-be-classified hyperspectral image; (2) building a hierarchical spatial similarity matrix; (3) obtaining a superpixel set; (4) building a mark sample dictionary; (5) solving a sparse coefficient matrix; (6) classifying superpixels; (7) outputting the classification results of the to-be-classified hyperspectral image. The method is advantageous in maintaining the spatial consistency of homogeneous regions of the hyperspectral image and being high in classification speed, and can be used for the rapid classification of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral remote sensing object classification method based on superpixel tensor sparse coding in the technical field of hyperspectral image classification. The invention can be used for fast ground object classification on hyperspectral remote sensing images. Background technique [0002] At present, the focus of research on hyperspectral data is mainly two aspects of dimensionality reduction and classification. Among them, the classification is mainly to process the data according to the different characteristic information of different types of data, and assign a label to each pixel, so as to realize the classification and recognition of the ground objects. According to whether there are labeled samples to participate in the processing process, classification methods can be divided into unsupervised classification methods, supervised classification method...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that in the process of solving the sparse coefficient, it is necessary to solve the sparse coefficient on the mapped dictionary for each mapped sample. would create a large computational burden

The disadvantage of this method is that it does not take into account the spatial information between adjacent samples, which will cause poor spatial consistency in homogeneous regions and affect the recognition accuracy of hyperspectral images.

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