High spectral remote sensing image classification method and system based on three-dimensional Gabor feature selection

Abstract

The invention is suitable for high spectral remote sensing image classification, and provides a high spectral remote sensing image classification method based on three-dimensional Gabor feature selection. The method comprises the following steps: A: according to a set frequency and a direction parameter value, generating a three-dimensional Gabor filter; B: carrying out convolution operation on the high spectral remote sensing image and the three-dimensional Gabor filter to obtain three-dimensional Gabor features; C: selecting a plurality of three-dimensional Gabor features which meet the requirements of each class of classification contribution degrees from the three-dimensional Gabor features; and D: using the selected three-dimensional Gabor features to classify the high spectral remote sensing images through a multi-task sparse classification method. The method is based on the three-dimensional Gabor features, wherein the adopted three-dimensional Gabor features comprise local change information with rich signals, and therefore, feature expression capability is high; the three-dimensional Gabor features are selected through a Fisher discriminant criterion, hidden high-level semantics among features can be fully utilized, redundant information is removed, and classification time complexity is lowered; and further, sparse coding is used to combine the three-dimensional Gabor features with multiple tasks to greatly improve classification precision.

Description

technical field [0001] The invention belongs to the field of data classification, in particular to a hyperspectral remote sensing image classification method and system based on three-dimensional Gabor feature selection and multi-task sparse representation. Background technique [0002] Hyperspectral remote sensing images are multispectral image data acquired by remote sensing sensors from objects of interest on the ground in the visible, near-infrared, mid-infrared and thermal infrared bands of the electromagnetic spectrum. Hyperspectral remote sensing images contain rich triple information of space and radiation spectrum, and show good results in the classification of ground materials. Traditional classification methods use commonly used classifiers (K nearest neighbors, support vector machines) to classify directly on hyperspectral remote sensing images, which cannot meet the actual classification effect. In view of the spatial and spectral three-dimensional structure of...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a hyperspectral remote sensing image classification method and system based on multi-task sparse representation based on three-dimensional Gabor feature selection, aiming at solving a large number of problems in the classification of hyperspectral remote sensing images in the prior art. Redundant information that is not conducive to classification reduces classification accuracy and increases classification time complexity

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