High Spatial Resolution Remote Sensing Classification with Lightweight CNN Using Dilated Convolution

High spatial resolution remote sensing (HSRRS) classification is one of the most promising topics in the field of remote sensing. Recently, convolutional neural network (CNN), as a method with superior performance, has achieved amazing results in the HSRRS classification tasks. However, the previously proposed CNN models generally have large model sizes, and thus require a tremendous amount of computing and consume a lot of memory. Furthermore, wearable electronic devices with limited hardware resources in actual application scenarios fail to meet the storage and computing requirements of these complex CNNs. To solve these problems, lightweight processing is important and practical. This paper proposes a new method called DC-LW-CNN, which applies Dilated Convolution (DC) and neuron pruning methods to maintain the classification performance of CNN and reduce resource consumption. The simulation results indicate that different DC-LW-CNNs have better performance effects in HSRRS classification task. Not only that, they also achieve this remarkable performance with smaller model size, less memory, and faster feedforward computing speed.