Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S

Volume:2017-July

Page Number:823-826

Key Words:Data fusion; image classification; deep learning; synthetic aperture radar (SAR) image; multispectral image

Abstract:Classification of multisensor data provides potential advantages over a single sensor in accuracy. In this paper, deep bimodal autoencoders are proposed for classification of fusing synthetic aperture radar (SAR) and multispectral images. The proposed deep network based on autoencoders is trained to discover both independencies of each modality and correlations across the modalities. Specifically, the sparse encoding layers in the front are applied to learn features of each modality, then shared representation layers in the middle are developed to learn fused features of two modalities, finally softmax classifier in the top is adopted for classification. Experimental results demonstrate that the proposed network is able to yield superior classification performance compared with some related networks.