A Multi-Branch Feature Fusion Network for Building Detection in Remote Sensing Images

Deep convolutional neural networks (CNNs) based methods have achieved great success in object detection, but when it comes to remote sensing images, new challenges appear considering that current approaches either downsampling or cropping on remote sensing images can hardly provide sufficient structural or semantic information. Aiming to these issues, we propose a multi-branch feature fusion network, in which a global branch and a local branch takes downsampled images and cropped patches as inputs and extracts contextual and structural features respectively. The extracted features are fused and composed into enhanced feature maps. A set of experiments over public building detection dataset for remote sensing images demonstrate the superiority of the proposed method over other state-of-art methods.