Remote sensing hyperspectral image classification method based on semi-supervised kernel adaptive learning

Abstract

The invention discloses a remote sensing hyperspectral image classification method based on semi-supervised kernel adaptive learning, relating to a classification method of remote sensing hyperspectral images and solving the problem of low resolution ratio existing in the traditional remote sensing hyperspectral image classification method. The remote sensing hyperspectral image classification method comprises the steps of: judging a labeling form of a hyperspectral image training sample set, acquiring an optimized target function, and then acquiring an optimal parameter or a data dependency kernel parameter; obtaining an optimal kernel function with an invariant structure or a variable structure according to the acquired parameter, further obtaining an optimal semi-supervised classifier, and realizing the classification of the remote sensing hyperspectral images by utilizing the classifier. The invention can accurately classify the end members of the remote hyperspectral images, improves the resolution ratio of the remote sensing hyperspectral images, and can be applied to the technical field of terrain military target reconnaissance, high-efficient warfare striking effect estimation, navy submarine real-time maritime environment monitoring and emergency responses of emergent natural disasters.

Description

technical field [0001] The invention relates to a classification method of remote sensing hyperspectral images. Background technique [0002] Hyperspectral images not only have many bands, but also generally observe complex types of objects, which can be divided into multiple types of objects such as vegetation, fields, buildings, roads, waters, swamps, and bare soil. If each pixel represents a type of surface object, then the pixel is called an end-member. In order to improve the image resolution, it is necessary to classify the endmembers to distinguish different ground objects. At present, the commonly used hyperspectral image endmember classification algorithms can be divided into supervised and unsupervised algorithms. The former is a classification algorithm for judging the classification of each endmember with known ground object types, while the latter relies purely on unknown ground object types. Spectral statistical differences were classified. Commonly used sup...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of low resolution in the current remote sensing hyperspectral image classification method, and provide a remote sensing hyperspectral image classification method based on semi-supervised kernel adaptive learning

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