Abstract:
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough d...Show MoreMetadata
Abstract:
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough deep, which might not extract very discriminant features for classification. In addition, they do not consider strong correlations among different hierarchical layers. Due to the two problems, a hybrid deep residual network is presented for HSIs classification in this paper. The proposed method firstly employs deep residual network (DRN) to extract very deep and discriminant features of HSIs. The DRN can help to overcome the decrease of classification accuracy that is caused by the increasing network depth and limited available training samples. Moreover, by incorporating different hierarchical features of network with a hybrid mechanism, the classification results can be further improved. Experimental results on a real hyperspectral image demonstrate that the proposed method outperforms other competitive methods.
Date of Conference: 23-28 July 2017
Date Added to IEEE Xplore: 04 December 2017
ISBN Information:
Electronic ISSN: 2153-7003