中文    English
当前位置: 本站首页 » 科学研究 » 科研成果 » 正文

钟燕飞--Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

发布日期:2016-11-30 21:58:00 阅读次数:[1742]次 作者:

核心提示:来源出版物: JOURNAL OF APPLIED REMOTE SENSING

作者: Zhong, YF (Zhong, Yanfei); Fe, F (Fe, Feng); Zhang, LP (Zhang, Liangpei)
来源出版物: JOURNAL OF APPLIED REMOTE SENSING  卷: 10  文献号: 025006  DOI: 10.1117/1.JRS.10.025006  出版年: APR 25 2016
摘要: The increase of the spatial resolution of remote-sensing sensors helps to capture the abundant details related to the semantics of surface objects. However, it is difficult for the popular object-oriented classification approaches to acquire higher level semantics from the high spatial resolution remote-sensing (HSR-RS) images, which is often referred to as the "semantic gap." Instead of designing sophisticated operators, convolutional neural networks (CNNs), a typical deep learning method, can automatically discover intrinsic feature descriptors from a large number of input images to bridge the semantic gap. Due to the small data volume of the available HSR-RS scene datasets, which is far away from that of the natural scene datasets, there have been few reports of CNN approaches for HSR-RS image scene classifications. We propose a practical CNN architecture for HSR-RS scene classification, named the large patch convolutional neural network (LPCNN). The large patch sampling is used to generate hundreds of possible scene patches for the feature learning, and a global average pooling layer is used to replace the fully connected network as the classifier, which can greatly reduce the total parameters. The experiments confirm that the proposed LPCNN can learn effective local features to form an effective representation for different land-use scenes, and can achieve a performance that is comparable to the state-of-the-art on public HSR-RS scene datasets. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)


版权所有:测绘遥感信息工程国家重点实验室   
联系地址: 中国·武汉市珞瑜路129号   邮编: 430079   E-mail:liesmars@whu.edu.cn
Tel/Fax:027-68778969(办公室) 027-68778229(国际交流办公室)027-68778525(研究生管理办公室)