Hyperspectral image classification and wave band selection method based on multi-target immune cloning

Abstract

The invention discloses a hyperspectral image classification and wave band selection method based on multi-target immune cloning. The hyperspectral image classification and wave band selection method based on multi-target immune cloning comprises the following steps that a sparse representation classifier is used for classifying hyperspectral remote sensing images so as to obtain classified images based on pixels; the hyperspectral remote sensing images are preprocessed by means of mean shift, the processed images are segmented into a plurality of clustering results through a multi-target immune cloning clustering and wave band selection algorithm, and the optimal clustering results are selected from the clustering results so as to constitute a segmentation image; the obtained classified images and the obtained segmentation image are fused by means of the maximum voting rule so as to obtain a final result image. The hyperspectral image classification and wave band selection method based on multi-target immune cloning has the advantages that the very high accuracy rate and Kappa coefficient values can be obtained under the circumstance that few training samples exist, region consistency is well kept, the hyperspectral image classification and wave band selection method is suitable for multiple hyperspectral data, and parameters are adjusted easily and conveniently.

Description

technical field [0001] The invention belongs to the field of machine learning and hyperspectral remote sensing image processing, and relates to the application of a method of space-spectrum combination and simultaneous feature selection in the field of hyperspectral remote sensing image processing, specifically a hyperspectral based on multi-target immune cloning Methods for image classification and band selection. Background technique [0002] The rapid development of modern remote sensing technology provides a convenient way for hyperspectral remote sensing image processing. challenge. Common pixel-based classification methods include maximum likelihood estimation, Bayesian estimation, neural networks, decision trees, genetic algorithms, and kernel-based methods. The characteristic of these methods is to use spectral features, texture information, or perform linear or non-linear transformation of these features to obtain new features and classify the corresponding pixels...

AI Technical Summary

Problems solved by technology

According to the characteristics of hyperspectral remote sensing images, spectral redundancy will not only increase the difficulty of calculation, but also affect the effect of classification

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