Because the reliability of feature for every pixel determines the accuracy of classification, it is important to design a\nspecialized feature mining algorithm for hyperspectral image classification. We propose a feature learning algorithm,\ncontextual deep learning, which is extremely effective for hyperspectral image classification. On the one hand, the\nlearning-based feature extraction algorithm can characterize information better than the pre-defined feature\nextraction algorithm. On the other hand, spatial contextual information is effective for hyperspectral image\nclassification. Contextual deep learning explicitly learns spectral and spatial features via a deep learning architecture\nand promotes the feature extractor using a supervised fine-tune strategy. Extensive experiments show that the\nproposed contextual deep learning algorithm is an excellent feature learning algorithm and can achieve good\nperformance with only a simple classifier.
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