Release Time:2019-03-09  Hits:

Indexed by: Journal Papers

Date of Publication: 2015-07-14

Journal: EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING

Included Journals: Scopus、EI、SCIE

Volume: 2015

Issue: 1

ISSN: 1687-5281

Key Words: Hyperspectral image classification; Contextual deep learning; Multinomial logistic regression (MLR); Supervised classification

Abstract: 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.