Desert classification based on a multi-scale residual network with an attention mechanism

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.