Abstract
Desert classification is the fundamental for preventing and/or controlling desertification. Topographical features of desert remote sensing images change constantly due to the uncertainty of desert terrain, illumination, and other properties. Therefore, it is a very challenging task to accurately classify desert areas. In order to quickly and accurately classify desert from remote sensing images, this paper proposed a multi-scale residual network based on an attention mechanism. The network used conventional convolutions to perform preliminary feature extraction on images, and subsequently adopted a multi-scale residual module to further process the feature maps. Based on the idea of fusing multi-scale features, the multi-scale residual module effectively reduced information loss and possible gradient disappearance because of using skip connections. By introducing the attention mechanism, dependencies between feature channels were established, as a result, the network could recalibrate channel characteristic responses adaptively. Experimental results showed that the proposed network had better generalization ability and a higher accuracy on classification of multispectral desert remote sensing images compared with other methods.
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Acknowledgments
This work is supported in part by, the National Natural Science Foundation of PR China (61773219), the Natural Science Foundation of Jiangsu (BK20161533), and Qing Lan Project of Jiangsu Province. The authors declare no conflict of interest.
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Weng, L., Wang, L., Xia, M. et al. Desert classification based on a multi-scale residual network with an attention mechanism. Geosci J 25, 387–399 (2021). https://doi.org/10.1007/s12303-020-0022-y
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DOI: https://doi.org/10.1007/s12303-020-0022-y