High-Resolution Aerial Images Semantic Segmentation Using Deep Fully Convolutional Network With Channel Attention Mechanism

Abstract

Semantic segmentation is one of the fundamental tasks in understanding high-resolution aerial images. Recently, convolutional neural network (CNN) and fully convolutional network (FCN) have achieved excellent performance in general images' semantic segmentation tasks and have been introduced to the field of aerial images. In this paper, we propose a novel deep FCN with channel attention mechanism (CAM-DFCN) for high-resolution aerial images' semantic segmentation. The CAM-DFCN architecture follows the mode of encoder–decoder. In the encoder, two identical deep residual networks are both divided into multiple levels and acted on spectral images and auxiliary data, respectively. Then, the feature map concatenation is carried out at each level. In the decoder, the channel attention mechanism (CAM) is introduced to automatically weigh the channels of feature maps to perform feature selection. On the one hand, the CAM follows the concatenated feature maps at each level to select more discriminative features for classification. On the other hand, the CAM is used to further weigh the semantic information and spatial location information in the adjacent-level concatenated feature maps for more accurate predictions. We evaluate the proposed CAM-DFCN by using two benchmarks (the Potsdam set and the Vaihingen set) provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that the proposed method has considerable improvement.