A Spatial Attention Guided Scene Classification Method for Multiscale Remote Sensing Dataset

Xinran Lyu, Libao Zhang

Abstract: Remote sensing image contains kinds of land cover. These surface coverings form complex and diverse scenes, which brings difficulties to scene classification. In recent years, many methods based on deep learning have achieved remarkable results. The existing researches mainly focus on training convolutional neural networks. However, these methods do not clearly distinguish the key information and redundant information of the images. Inspired by the attention mechanism, we propose a scene classification network, which combines the residual unit and spatial attention mechanism. It automatically allocates large weights to the key regions of the image, so it can ignore the redundant information adaptively. In addition, we designed a multi -scale classification result voting strategy for dataset with images of different scales to improve classification accuracy. We evaluated the proposed approach with four state-of-the-art methods on the two datasets. Experimental results show that the proposed model has achieved the best classification performance.