Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification

High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.