A Lightweight and Multi-Scale CNN Model for Land-Cover Classification with High-Resolution Remote Sensing Images

Accurate and timely landcover information plays an important role in land resources management and urban planning. In this article, a lightweight and multi-scale convolutional neural network model is proposed for high-resolution remote sensing images classification. Its inputs are the multi -scale patches to capture the multi -scale variability of spatial features in high-resolution remote sensing images. In this model, some standard convolution is replaced by depth separable convolution, which has fewer parameters and requires less calculation cost. The experiments were conducted on high-resolution images of two different regions (i.e., Beijing and Qingdao, China). The promising performance verified the proposed method is very efficient for the classification of high-resolution remote sensing imagery.