@inproceedings{eceff38ec02a4c1ab83ea06fa9f72a98,
title = "Classification of fusing SAR and multispectral image via deep bimodal autoencoders",
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.",
keywords = "Data fusion, deep learning, image classification, multispectral image, synthetic aperture radar (SAR) image",
author = "Jie Geng and Hongyu Wang and Jianchao Fan and Xiaorui Ma",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127079",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "823--826",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}