Wave band selection method for hyperspectral remote-sensing image

Abstract

The invention discloses a wave band selection method for a hyperspectral remote-sensing image. The wave band selection method improves the traditional wave band selection method for the hyperspectral remote-sensing image, which is analyzed on the basis of the important point of a time sequence. The wave band selection method for the hyperspectral remote-sensing image comprises the following steps: firstly, on the basis of visual clustering tendency evaluation, clustering by a spectral clustering algorithm to reduce a clustering number search range and improve the clustering quality; then, when an important redundant wave band is finally reduced, removing parts of a high-redundancy wave band according to the condition mutual information among wave bands; and searching an optimal wave band combination with a branch and bound method to improve the classification precision and reduce a final wave band number. Compared with the prior art, the wave band selection method for the hyperspectral remote-sensing image, which is disclosed by the invention, has a bigger advantage on the aspects of finally-selected wave band numbers and the corresponding classification correction rate, and the required calculation time is far lower than calculation time required with most traditional methods.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for selecting bands of hyperspectral remote sensing images. Background technique [0002] With the rapid development of aerospace technology and remote sensing science, the available hyperspectral remote sensing data is increasing at an alarming rate. How to effectively deal with it and make full use of the rich information contained in it is one of the hot topics in the field of remote sensing and pattern recognition. However, hyperspectral images are often composed of hundreds of continuous bands. The large number of bands, high correlation and redundancy between bands bring a huge amount of calculation to further processing and analysis, thus making the problem extremely complicated. [0003] The most important preprocessing for processing hyperspectral remote sensing data is to reduce the dimensionality of many bands. Common methods include feature extraction and fea...

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Problems solved by technology

[0019] The purpose of the present invention is to overcome the shortcomings of the existing hyperspectral remote sensing image band selection method based on time series important point analysis that the K-means cl

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