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

A technology that combines kernel functions and morphological features, applied to instruments, character and pattern recognition, computer components, etc., can solve problems that have not been reported

Inactive Publication Date: 2014-03-12
孙琤
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In the field of remote sensing image processing, the research and application of the combined kern

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

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Embodiment Construction

[0079] The invention proposes a new combined kernel function RVM hyperspectral classification method that combines multi-scale morphological features. Using the method of the present invention to carry out real hyperspectral data classification experiments, the combined kernel function RVM classification method was compared with the traditional RVM classification method based solely on spectral features in terms of accuracy and sparseness.

[0080] The present invention will be further described in detail below in conjunction with the accompanying drawings and preferred embodiments. The computer hardware environment that described experiment adopts is Intel Core2 dual-core CPU, 1.58GHz / 3.25GB internal memory. The software environment is Microsoft Windows XP, Matlab R2008a. The algorithm described in the present invention is realized by MATLAB R2008a.

[0081]The correspondence between constituent elements in the claims and specific examples in the embodiments can be exemplif...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 孙琤
Owner 孙琤
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