A Remote Sensing Land Cover Classification Algorithm Based on Attention Mechanism. Issue 6 (2nd November 2021)

• Record Type: Journal Article
• Authors: Zhang, Xiaolu; Wang, Zhaoshun; Cao, Lianyu; Wang, Mang
• Is Part Of: Canadian journal of remote sensing. Volume 47:Issue 6(2021)
• Page Start: 835
• Page End: 845
• Publication Date: 2021-11-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2021.1971961
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: The Deeplabv3+ network for semantic segmentation of remote sensing images has drawbacks like inaccurate edge segmentation and intra-class inconsistency in large-scale segmentation. The attention mechanism is a good solution to this problem. This paper integrates the dual attention mechanism module including spatial attention mechanism and channel attention mechanism (ISNet) into the network. Furthermore, the ISNet is added to both the encoder part and decoder part in the network. The newly proposed network is called DISNet. It can enhance target features and suppress background features, having the potential to improve semantic segmentation accuracy. The experimental results indicate that the Mean Intersection over Union (mIoU) of the Deepglobe dataset, GID dataset, and ISPRS test project (Vaihingen) dataset is respectively 64.22, 62.83, and 78.59%. Those results are 5.68, 4.80, and 6.20% higher than that of Deeplabv3+. The proposed network can effectively improve the accuracy of remote sensing land cover classification.