3 November 2016 An improved hyperspectral image classification approach based on ISODATA and SKR method
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Abstract
Hyper-spectral images can not only provide spatial information but also a wealth of spectral information. A short list of applications includes environmental mapping, global change research, geological research, wetlands mapping, assessment of trafficability, plant and mineral identification and abundance estimation, crop analysis, and bathymetry. A crucial aspect of hyperspectral image analysis is the identification of materials present in an object or scene being imaged.

Classification of a hyperspectral image sequence amounts to identifying which pixels contain various spectrally distinct materials that have been specified by the user. Several techniques for classification of multi-hyperspectral pixels have been used from minimum distance and maximum likelihood classifiers to correlation matched filter-based approaches such as spectral signature matching and the spectral angle mapper.

In this paper, an improved hyperspectral images classification algorithm is proposed. In the proposed method, an improved similarity measurement method is applied, in which both the spectrum similarity and space similarity are considered. We use two different weighted matrix to estimate the spectrum similarity and space similarity between two pixels, respectively. And then whether these two pixels represent the same material can be determined. In order to reduce the computational cost the wavelet transform is also applied prior to extract the spectral and space features.

The proposed method is tested using hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory. Experimental results the efficiency of this new method on hyperspectral images associated with space object material identification.
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Pu Hong, Xiao-feng Ye, Hui Yu, Zhi-jie Zhang, Yu-fei Cai, Xin Tang, Wei Tang, Chensheng Wang, "An improved hyperspectral image classification approach based on ISODATA and SKR method", Proc. SPIE 10030, Infrared, Millimeter-Wave, and Terahertz Technologies IV, 100301W (3 November 2016); doi: 10.1117/12.2245919; https://doi.org/10.1117/12.2245919
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