Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks

Abstract

Remote sensing image scene interpretation has many applications on land use land covers; thanks to many satellite technologies innovations that generate high-quality images periodically for analysis and interpretation through computer vision techniques. In recent literature, deep learning techniques have demonstrated to be effective in image feature learning thus aiding several computer vision applications on land use land cover. However, most deep learning techniques suffer from problems such as the vanishing gradients, network over-fitting, among other challenges of which the different literature works have attempted to address from varying perspectives. The goal of machine learning in remote sensing is to learn image feature patterns extracted by computer vision techniques for scene classification tasks. Many applications that utilize data from remote sensing are on the surge, this include, aerial surveillance and security, smart farming, among others. These applications require to process satellite image information effectively and reliably for appropriate responses. This research proposes the deep residual feature learning network that is effective in image feature learning which can be utilizable in a networked environment for appropriate decision making processes. The proposed strategy utilizes short-cut connections and mapping functions for deep feature learning. The proposed technique is evaluated on two publicly available remote sensing datasets and it attains superior classification accuracy results of 96.30% and 92.56% respectively on the Ucmerced and Whu-siri datasets, improving the state-of-the-art significantly.