دورية أكاديمية
A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection
| العنوان: | A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection |
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| المؤلفون: | Mengxi Liu, Zhuoqun Chai, Haojun Deng, Rong Liu |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4297-4306 (2022) |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Change detection (CD), cropland, deep learning (DL), remote sensing, transformer, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/9780164/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2022.3177235 |
| URL الوصول: | https://doaj.org/article/a60e405cf1324f408a130b74f557aeae |
| رقم الأكسشن: | edsdoj.60e405cf1324f408a130b74f557aeae |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21511535 |
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| DOI: | 10.1109/JSTARS.2022.3177235 |