Remote sensing image classification method based on partially random supervision discrete Hash

Abstract

The invention discloses a remote sensing image classification method based on partially random supervision discrete Hash. The method comprises the following steps that: according to calibrated real data, carrying out object segmentation on a remote sensing image, and carrying out characteristic extraction on a segmented object, wherein each object is represented by a characteristic vector, and thecharacteristic vectors of all objects are combined into a characteristic matrix; according to the same ratio, dividing each category of samples into training samples and testing samples; carrying outdiscrete Hash coding on all samples; carrying out partially random Hash coding on all samples; combining the discrete Hash coding with the partially random Hash coding, carrying out iterative optimization on parameters, and finally, obtaining more accurate Hash coding; and according to the generated Hash coding, calculating Hamming distance to finish classification. By use of the method, the problem of high calculation complexity caused by an overlarge data size in a remote sensing image processing process is solved, and the remote sensing image can be quickly and effectively classified.

Description

technical field [0001] The invention relates to a remote sensing image classification method, in particular to a remote sensing image classification method based on partial random supervised discrete hash. Background technique [0002] Due to the rapid development of satellite and aircraft technology, the application of remote sensing data has become more and more extensive, and target classification has gradually become one of the most important tasks in remote sensing data analysis. However, with the significant increase in data volume and resolution of remote sensing images, object classification has also become more challenging. Therefore, an effective feature representation method is very meaningful for remote sensing image target classification. In recent years, many technologies in this area have been proposed, which can be roughly divided into three categories: methods based on manual features, methods based on deep feature learning, and methods based on unsupervise...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a remote sensing image classification method, aiming at the problem of large amount of remote sensing image data and complex calculation, combined with data independent and data dependent methods, to complete the accurate classification of remote sensing image hash representation

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