期刊
REMOTE SENSING
卷 14, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/rs14030750
关键词
grassland remote sensing monitoring; deep learning; multi-spectral and synthetic aperture radar data; convolutional neural network; adaptive feature fusion
类别
资金
- National Key Research and Development Program of China [2018YFE0122700]
- Provincial Natural Science Foundation Project [ZR2021MC099]
Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. In this study, we verified the advantages of fusing multi-spectral (MS) and synthetic aperture radar (SAR) data for improving the accuracy of grassland remote sensing monitoring, especially in cloud-covered areas. Results showed that the proposed adaptive feature fusion method with single-size patches achieved the best results.
Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. Therefore, the remote monitoring of grasslands is an important tool to control degradation and protect grasslands. However, the existing methods are often disturbed by clouds and fog, which makes it difficult to achieve all-weather and all-time grassland remote sensing monitoring. Synthetic aperture radar (SAR) data can penetrate clouds, which is helpful for solving this problem. In this study, we verified the advantages of the fusion of multi-spectral (MS) and SAR data for improving classification accuracy, especially for cloud-covered areas. We also proposed an adaptive feature fusion method (the SK-like method) based on an attention mechanism, and tested two types of patch construction strategies, single-size and multi-size patches. Experiments have shown that the proposed SK-like method with single-size patches obtains the best results, with 93.12% accuracy and a 0.91 average f1-score, which is a 1.02% accuracy improvement and a 0.01 average f1-score improvement compared with the commonly used feature concatenation method. Our results show that the all-weather, all-time remote sensing monitoring of grassland is possible through the fusion of MS and SAR data with suitable feature fusion methods, which will effectively enhance the regulatory capability of grassland resources.
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