Multi-Scale Spatial and Channel-wise Attention for Improving Object Detection in Remote Sensing Imagery

Abstract

The spatial resolution of remote sensing images is continuously improved by the development of remote sensing satellite and sensor technology. Hence, background information in an image becomes increasingly complex and causes considerable interference to the object detection task. Can we pay as much attention to the object in an image as human vision does? This letter proposes a multi-scale spatial and channel-wise attention (MSCA) mechanism to answer this question. MSCA has two advantages that help improve object detection performance. First, attention is paid to the spatial area related to the foreground, and compared with other channels, more attention is given to the feature channel with a greater response to the foreground region. Second, for objects with different scales, MSCA can generate an attention distribution map that integrates multi-scale information and applies it to the feature map of the deep network. MSCA is a flexible module that can be easily embedded into any object detection model based on deep learning. With the attention exerted by MSCA, the deep neural network can efficiently focus on objects of different backgrounds and sizes in remote sensing images. Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model.