Improving Semantic Segmentation in Aerial Imagery via Graph Reasoning and Disentangled Learning

Semantic segmentation in aerial imagery is still an important, yet challenging task due to the complex characteristics of remote-sensing data. The critical issues consist of: 1) extreme foreground-background imbalance; 2) large intra-class variance; and 3) arbitrary-oriented, dense, and small object...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-18
Main Authors: Niu, Ruigang, Sun, Xian, Tian, Yu, Diao, Wenhui, Feng, Yingchao, Fu, Kun
Format: Article
Language:English
Subjects:
Aerial imagery
Aerial photography
Cognition
Context modeling
disentangled learning
feature alignment
Feature extraction
graph convolutional networks (GCNs)
Image processing
Image segmentation
Imagery
Learning
Methods
Reasoning
Remote sensing
Semantic segmentation
Semantics
Task analysis
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Summary:Semantic segmentation in aerial imagery is still an important, yet challenging task due to the complex characteristics of remote-sensing data. The critical issues consist of: 1) extreme foreground-background imbalance; 2) large intra-class variance; and 3) arbitrary-oriented, dense, and small objects. The above challenges make it unlikely to model the effective global interdependencies of semantic heterogeneous regions. Besides, general semantic segmentation methods suffer from feature ambiguity due to the joint feature learning paradigm, leading to inferior detail information. In this article, we propose an improved semantic segmentation framework to tackle these problems via graph reasoning (GR) and disentangled learning. On the one hand, a simple, yet effective GR unit is introduced to implement coordinate-interaction space mapping and perform relation reasoning over the graph. It can be deployed on the feature pyramid network (FPN) to exploit cross-stage multi-scale information. On the other hand, we propose a so- called disentangled learning paradigm to explicitly model the foreground and boundary objects, instantiated as foreground prior estimation (FPE) and boundary alignment (BA). The indication of the intermediate feature can be effectively emphasized to enhance the discriminative abilities of the network. Extensive experiments over iSAID, ISPRS Vaihingen, and the general Cityscapes datasets demonstrate the effectiveness and efficiency of the proposed framework over other state-of-the-art semantic segmentation methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3121471