Classification of hyperspectral data with ensemble of subspace ICA and edge-preserving filtering

Junshi Xia 1 Lionel Bombrun 1 Tülay Adali 2 Yannick Berthoumieu 1 Christian Germain 1
1 IMS - Laboratoire de l'intégration, du matériau au système
2 MLSP lab - Machine Learning for Signal Processing Laboratory
Abstract : Conventional feature extraction methods cannot fully exploit both the spectral and spatial information of hyperspectral imagery. In this paper, we propose an ensemble method of subspace independent component analysis (ICA) and edge-preserving filtering (EPF) for the classification of hyper-spectral data to achieve this task. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectral independent components followed by a recent and effective EPF method, rolling guidance filter (RGF), to produce spatial features. The spatial features are treated as the input of a random forest (RF) classifier. Finally , the classification results from each subset are integrated together to produce the final map. Experimental results on real hyperspectral data demonstrate the effectiveness of the proposed method. A sensitivity analysis of this new classifier is also performed.
Keywords : classification hyperspectral data independent component analysis edge preserving filter
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Conference papers
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Junshi Xia, Lionel Bombrun, Tülay Adali, Yannick Berthoumieu, Christian Germain. Classification of hyperspectral data with ensemble of subspace ICA and edge-preserving filtering. 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. ⟨hal-01315337⟩

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