A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the la...

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Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing Jg. 15; S. 4297 - 4306
Hauptverfasser: Liu, Mengxi, Chai, Zhuoqun, Deng, Haojun, Liu, Rong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Agglomeration
Aggregation
Agricultural ecosystems
Agricultural land
Biological system modeling
Change detection
Change detection (CD)
Computer applications
Context
cropland
Data mining
Datasets
Decoding
Deep layer
deep learning (DL)
Detection
Feature extraction
Food security
Head
High resolution
Image resolution
Remote sensing
Resolution
Spatial discrimination
Spatial resolution
Task analysis
transformer
Transformers
ISSN:1939-1404, 2151-1535
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Zusammenfassung:Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intraclass differences of cropland changes in high-resolution images. In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, in this article, we propose a CNN-transformer network with multiscale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland CD. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based MSCA is designed to encode and aggregate context information. Finally, a multibranch prediction head with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of CD dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset, which contains 600 pairs of 512 × 512 bi-temporal images with the spatial resolution of 0.5-2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic CD dataset, and of 71.29% on CLCD.
Bibliographie:ObjectType-Article-1
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2022.3177235