High-resolution remote sensing image semi-supervised multi-view feature selection method with tag learning

Abstract

The invention provides a high-resolution remote sensing image semi-supervised multi-view feature selection method with tag learning, belongs to the technical field of high-resolution remote sensing image semi-supervised feature selection, and solves the problems in the existing high-resolution remote sensing image feature selection methods that supervised and unsupervised methods cannot achieve good enough performance when there are only a few objects are marked and views are not available in the features of high-resolution images. The method comprises the following steps that 1. original image feature sets are acquired so that feature groups are generated; 2. original feature weight coefficient vectors beta formed by the weight coefficients of all the feature vectors in all the feature groups are calculated through a class probability matrix yu and a diagonal matrix F having exclusive group information; and 3. iterative calculation of the feature weight coefficient vectors beta is performed, the preset number of the weight coefficients are selected, and all the feature vectors corresponding to the selected weight coefficients act as selected feature sets. The method is used for feature selection of the high-resolution remote sensing images.

Description

technical field [0001] The invention relates to a semi-supervised multi-view feature selection method for high-resolution remote sensing images with label learning, and belongs to the technical field of semi-supervised feature selection for high-resolution remote sensing images. Background technique [0002] High-resolution image VHRs can capture small or narrow objects, so they can be used to develop a corresponding infrastructure for continuous monitoring and map updating. This application often uses the object-based image analysis method OBIA to exploit the spatial relationship between pixels to cope with the salt and pepper effect when using a pixel-wise classifier. OBIA can extract a large number of features. Since not all features are beneficial for classification and classification performance may degrade as the number of features increases, feature selection becomes an important issue to address this issue. [0003] Spatial, texture and shape features can be extrac...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that in the existing high-resolution remote sensing image feature selection methods, when only a small number of objects are marked, supervised and unsupervised methods cannot achieve good enough performance and views are not available in the features of high-resolution images. problem, providing a semi-supervised multi-view feature selection method for high-scoring remote sensing images with label learning

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