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论文类型：期刊论文

发表时间：2015-07-14

发表刊物：EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING

收录刊物：SCIE、EI、Scopus

卷号：2015

期号：1

ISSN号：1687-5281

关键字：Hyperspectral image classification; Contextual deep learning; Multinomial logistic regression (MLR); Supervised classification

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