Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 18, Issue 11, Pages 1926-1930Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3011405
Keywords
Remote sensing; Feature extraction; Convolution; Training; Neural networks; Standards; Semantics; Aerial image dataset (AID); attention; convolutional neural network (CNN); NWPU-RESISC45; remote sensing; scene classification
Categories
Funding
- National Key Research and Development Program of China [2017YFC0505205]
- National Research and Development Infrastructure and Facility Development Program of China [DKA2018-12-02-XX]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19020104]
- IT Integrated Service Platform of Sichuan Wolong Natural Reserve
- Biodiversity Investigation, Observation and Assessment Program of Ministry of Ecology and Environment of China
Ask authors/readers for more resources
The letter presents an enhanced attention module (EAM) to improve the feature extraction and generalization abilities of deep neural networks for remote sensing image classification, achieving competitive performance and state-of-the-art results on various datasets.
Classifying different satellite remote sensing scenes is a very important subtask in the field of remote sensing image interpretation. With the recent development of convolutional neural networks (CNNs), remote sensing scene classification methods have continued to improve. However, the use of recognition methods based on CNNs is challenging because the background of remote sensing image scenes is complex and many small objects often appear in these scenes. In this letter, to improve the feature extraction and generalization abilities of deep neural networks so that they can learn more discriminative features, an enhanced attention module (EAM) was designed. Our proposed method achieved very competitive performance-94.29% accuracy on NWPU-RESISC45 and state-of-the-art performance on different remote sensing scene recognition data sets. The experimental results show that the proposed method can learn more discriminative features than state-of-the-art methods, and it can effectively improve the accuracy of scene classification for remote sensing images. Our code is available at https://github.com/williamzhao95/Pay-More-Attention.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available