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A two-stage method for spectral–spatial classification of hyperspectral images

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Abstract

We propose a novel two-stage method for the classification of hyperspectral images. Pixel-wise classifiers, such as the classical support vector machine (SVM), consider spectral information only. As spatial information is not utilized, the classification results are not optimal and the classified image may appear noisy. Many existing methods, such as morphological profiles, superpixel segmentation, and composite kernels, exploit the spatial information. In this paper, we propose a two-stage approach inspired by image denoising and segmentation to incorporate the spatial information. In the first stage, SVMs are used to estimate the class probability for each pixel. In the second stage, a convex variant of the Mumford–Shah model is applied to each probability map to denoise and segment the image into different classes. Our proposed method effectively utilizes both spectral and spatial information of the data sets and is fast as only convex minimization is needed in addition to the SVMs. Experimental results on three widely utilized real hyperspectral data sets indicate that our method is very competitive in accuracy, timing, and the number of parameters when compared with current state-of-the-art methods, especially when the inter-class spectra are similar or the percentage of training pixels is reasonably high.

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Acknowledgements

The authors would like to thank the Computational Intelligence Group from the Basque University for sharing the hyperspectral data sets in their website (http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes), Prof. Leyuan Fang from College of Electrical and Information Engineering at Hunan University for providing the programs of the SC-MK and MFASR methods in his homepage (http://www.escience.cn/people/LeyuanFang) and Prof. Xudong Kang from College of Electrical and Information Engineering at Hunan University for providing the program of the EPF method in his homepage (http://xudongkang.weebly.com/). Raymond H. Chan’s research is supported by HKRGC Grants No. CUHK14306316, CityU Grant: 9380101, CRF Grant C1007-15G, AoE/M-05/12. Kelvin K. Kan’s research is supported by US Air Force Office of Scientific Research under grant FA9550-15-1-0286. Mila Nikolova’s research is supported by the French Research Agency (ANR) under grant No ANR-14-CE27-001 (MIRIAM) and by the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the programme Variational Methods and Effective Algorithms for Imaging and Vision, EPSRC grant no EP/K032208/1. Robert J. Plemmons’ research is supported by HKRGC Grant No. CUHK14306316 and US Air Force Office of Scientific Research under grant FA9550-15-1-0286.

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Authors and Affiliations

  1. Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong

    Raymond H. Chan

  2. Department of Mathematics, Emory University, Atlanta, GA, 30322, USA

    Kelvin K. Kan

  3. CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235, Cachan, France

    Mila Nikolova

  4. Department of Computer Science and Department of Mathematics, Wake Forest University, Winston-Salem, NC, 27106, USA

    Robert J. Plemmons

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  1. Raymond H. Chan
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In memory of Mila Nikolova.

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Chan, R.H., Kan, K.K., Nikolova, M. et al. A two-stage method for spectral–spatial classification of hyperspectral images. J Math Imaging Vis 62, 790–807 (2020). https://doi.org/10.1007/s10851-019-00925-9

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