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Open AccessArticle

Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

by Chu He 1,2,*,†, Qingyi Zhang 1,†, Tao Qu 3, Dingwen Wang 3 and Mingsheng Liao 2
1
School of Electronic Information, Wuhan University, Wuhan 430072, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
School of Computer Science, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(23), 2870; https://doi.org/10.3390/rs11232870
Received: 7 November 2019 / Revised: 28 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms. View Full-Text
Keywords: image classification; deep features; hand-crafted features; Sinkhorn loss image classification; deep features; hand-crafted features; Sinkhorn loss
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MDPI and ACS Style

He, C.; Zhang, Q.; Qu, T.; Wang, D.; Liao, M. Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance. Remote Sens. 2019, 11, 2870.

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