10 September 2014 Joint sparse hyperspectral image classification based on adaptive spatial context
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Hyperspectral image (HSI) analysis is attracting a growing interest in real-world applications, many of which can finally be transformed into classification tasks. Traditional spectral-spatial HSI classification methods take advantage of the identical spatial information that is available everywhere, but this is not always the case, especially in the class boundary. A method for HSI classification based on the spectral information and the adaptive spatial context is proposed. First, we introduce a high-dimensional steering kernel to describe the adaptive spatial context and select the spatial correlative pixels of a given test pixel according to the adaptive spatial context. The selected pixels can be simultaneously sparse represented by linear combinations of a few common training samples. Then, a classifier imposing the adaptive spatial context to determine the final label of the test pixel is proposed. Experimental results on real HSIs show that our algorithm outperforms other state-of-art algorithms.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yang Xu, Yang Xu, Zebin Wu, Zebin Wu, Zhi-Hui Wei, Zhi-Hui Wei, } "Joint sparse hyperspectral image classification based on adaptive spatial context," Journal of Applied Remote Sensing 8(1), 083552 (10 September 2014). https://doi.org/10.1117/1.JRS.8.083552 . Submission:

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