Combination-kernel-function RVM (Relevance Vector Machine) hyperspectral classification method integrated with multi-scale morphological characteristics

Abstract

The invention provides a combination-kernel-function RVM hyperspectral classification method integrated with multi-scale morphological characteristics. The method comprises the steps that 1) dimensions of hyperspectral images are reduced via principal component transform; 2) spatial characteristics of the hyperspectral images after principal component transform are extracted via mathematical morphological transform; 3) according to theories of the combination kernel function, combination kernel functions based on addition, multiplication and weighted addition are respectively established, and spectral and spatial characteristic of the images are integrated by means of a combination kernel function method; and 4) the hyperspectral images are classified via a combination-kernel-function RVM classifier, and classification experiments are carried out on the hyperspectral images via an AVIRIS (Airborne Visible Infrared Imaging Spectrometer). Compared with a traditional RVM classifier based on spectral characteristics, the classification precision of the combination-kernel-function RVM is greatly increased without substantial increase of training time; and the method of the invention is strongly stable and is not sensitive to the number of samples.

Description

technical field [0001] The invention relates to the technical field of hyperspectral image processing methods and applications, and more specifically, to a combined kernel function RVM hyperspectral classification method that integrates multi-scale morphological features. Background technique [0002] Hyperspectral images have attracted widespread attention at home and abroad due to their high spectral resolution. The spectral coverage of hyperspectral images ranges from visible light to near-infrared, and can obtain almost continuous band information of ground objects. On the one hand, the extremely high spectral resolution of hyperspectral images can identify finer object categories, but it also brings challenges to traditional remote sensing image classification methods. [0003] Traditional hyperspectral supervised classification methods include: maximum likelihood method, artificial neural network, etc. The parameter initialization of the neural network method is diff...

AI Technical Summary

Problems solved by technology

In the field of remote sensing image processing, the research and application of the combined kernel function RVM classifier combining spatial features and spectral features has not been reported.

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