TY - GEN
T1 - Broad-classifier for remote sensing scene classification with spatial and channel-wise attention
AU - Chen, Zhihua
AU - Liu, Yunna
AU - Zhang, Han
AU - Sheng, Bin
AU - Li, Ping
AU - Xue, Guangtao
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 61672228, 61370174) and Shanghai Automotive Industry Science and Technology Development Foundation (No. 1837).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Attention mechanism
KW - Broad learning system
KW - Remote sensing image
KW - Scene classification
UR - http://www.scopus.com/inward/record.url?scp=85096568318&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61864-3_23
DO - 10.1007/978-3-030-61864-3_23
M3 - Conference article published in proceeding or book
AN - SCOPUS:85096568318
SN - 9783030618636
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 275
BT - Advances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Stephanidis, Constantine
A2 - Papagiannakis, George
A2 - Wu, Enhua
A2 - Thalmann, Daniel
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 37th Computer Graphics International Conference, CGI 2020
Y2 - 20 October 2020 through 23 October 2020
ER -