Spatial-spectral metric learning for hyperspectral remote sensing image classification

Jiangtao Peng; Yicong Zhou; C. L. Philip Chen

doi:10.1117/12.2060309

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15 September 2014 Spatial-spectral metric learning for hyperspectral remote sensing image classification

Jiangtao Peng, Yicong Zhou, C. L. Philip Chen

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Proceedings Volume 9222, Imaging Spectrometry XIX; 92220K (2014) https://doi.org/10.1117/12.2060309
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

A spatial-spectral metric learning (SSML) framework for hyperspectral image (HSI) classification is proposed. SSML learns a metric by considering both the spectral characteristics and spatial features represented as the mean of neighboring pixels. It first performs the local pixel neighborhood preserving embedding (LPNPE) to reduce the dimensionality of HSI and meanwhile to preserve the spatial local similarity structure. Then, it learns a spectral and spatial distance metric, separately. Finally, the combination of the spectral and spatial metrics yields a joint spatial-spectral metric. It is followed by a nearest neighbor (NN) classifier for HSI classification. SSML shows good performance over the spectral and spatial NN and SVM on the benchmark hyperspectral data set of Indian Pines.

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Jiangtao Peng, Yicong Zhou, and C. L. Philip Chen "Spatial-spectral metric learning for hyperspectral remote sensing image classification", Proc. SPIE 9222, Imaging Spectrometry XIX, 92220K (15 September 2014); https://doi.org/10.1117/12.2060309

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