文献类型：期刊文献

英文题名：Broad-Classifier for Remote Sensing Scene Classification with Spatial and Channel-Wise Attention

作者：Chen, Zhihua[1]; Liu, Yunna[1]; Zhang, Han[1]; Sheng, Bin[2]; Li, Ping[3]; Xue, Guangtao[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; [3] Faculty of Information Technology, Macau University of Science and Technology, Macau, 999078, China

年份：2020

卷号：12221 LNCS

起止页码：267

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20204809544927)

语种：英文

外文关键词：Learning systems - Image enhancement - Classification (of information) - Semantics - Image classification - Military applications - Military photography

摘要：Remote sensing scene classification is an important technology, which is widely used in military and civil applications. However, it is still a challenging problem due to the complexity of scene images. Recently, the development of remote sensing satellite and sensor devices has greatly improved the spatial resolution and semantic information of remote sensing images. Therefore, we propose a novel remote sensing scene classification approach to enhance the performance of scene classification. First, a spatial and channel-wise attention module is proposed to adequately utilize the spatial and feature information. Compare with other methods, channel-wise module works on the feature maps with diverse levels and pays more attention to semantic-level features. On the other hand, spatial attention module promotes correlation between foreground and classification result. Second, a novel classifier named broad-classifier is designed to enhance the discriminability. It greatly reduces the cost of computing in the meantime by broad learning system. The experimental results have show that our classification method can effectively improve the average accuracies on remote sensing scene classification data sets. ? 2020, Springer Nature Switzerland AG.