A Lightweight and Multiscale Network for Remote Sensing Image Scene Classification

Abstract

Remote sensing image (RSI) scene classification plays an active role in many application areas. Due to the excellent performance of the convolutional neural networks (CNNs), which have widely applied in RSI scene classification in recent years. However, most existing methods improve the classification accuracy by improving the model parameters or fusing the features of CNNs. This will make the whole model very complicated and unable to extract multiscale features at a more granular level. This letter proposes a novel and lightweight multiscale depthwise network (MSDWNet) with efficient spatial pyramid attention (ESPA), namely ESPA-MSDWNet, with low model parameters and high accuracy in solving this problem. The ESPA-MSDWNet uses MobileNet V2 as a backbone. We represent multiscale features at a more granular level and expand the receptive fields by multiscale depthwise convolution (MSDW Conv). We also propose the ESPA module to extract dependencies between channels. The ablation experiment verifies the effectiveness of our proposed MSDW Conv and ESPA module. Experimental results on three public RSI datasets show that ESPA-MSDWNet has advantages in classification accuracy and execution efficiency over current state-of-the-art (SOTA) methods.