Paper Info
Title
Multi-label Aerial Image Classification using A Bidirectional Class-wise Attention Network
Abstract
Multi-label aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. However, one common limitation shared by existing methods is that the co-occurrence relationship of various classes, so called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively.
Year
DOI
Venue
2019
10.1109/JURSE.2019.8808940
2019 Joint Urban Remote Sensing Event (JURSE)
Keywords
Field
DocType
multi-label classification,high resolution aerial image,Convolutional Neural Network (CNN),class attention learning,Bidirectional Long Short-Term Memory (BiLSTM),class dependency
Pattern recognition,Computer science,Multi-label classification,Feature extraction,Aerial image,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2334-0932
978-1-7281-0010-4
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Yuansheng Hua1165.96
Lichao Mou225425.35
Xiao Xiang Zhu3896103.00