A2-FPN for semantic segmentation of fine-resolution remotely sensed images

The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing suc...

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Vydáno v:International journal of remote sensing Ročník 43; číslo 3; s. 1131 - 1155
Hlavní autoři: Li, Rui, Wang, Libo, Zhang, Ce, Duan, Chenxi, Zheng, Shunyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 01.02.2022
Taylor & Francis Ltd
Témata:
Agglomeration
Aggregation
attention mechanism
automation
data collection
Decision analysis
Decision making
deep learning
Defects
Feature extraction
Image resolution
Image segmentation
landscapes
Machine learning
Pattern analysis
Remote observing
Remote sensing
Resolution
Semantic segmentation
Semantics
ISSN:0143-1161, 1366-5901, 1366-5901
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Shrnutí:The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network ( -FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our -FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN .
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ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2022.2030071