DSPCANet: Dual-Channel Scale-Aware Segmentation Network With Position and Channel Attentions for High-Resolution Aerial Images

• Main Authors: Yun-Cheng Li, Heng-Chao Li, Wen-Shuai Hu, Hui-Ling Yu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Attention mechanism; deep learning; high-resolution aerial images; multiscale learning; semantic segmentation
• Online Access: https://ieeexplore.ieee.org/document/9507255/

As a basic research topic in the field of remote sensing, semantic segmentation of high-resolution aerial images has broad application prospects. However, most existing semantic segmentation methods usually extract multiscale features of images in a hierarchical manner and fail to make full use of the surface information from high-resolution remote sensing images. To address the above problem, we propose a novel dual-channel scale-aware segmentation network with position and channel attentions (DSPCANet) for high-resolution aerial images, which contains an Xception branch and a digital surface model-based position and channel attention fusion (DSMPCF) branch to process the near-infrared, red, and green (IRRG) spectral images and DSM images, respectively. First, the inner residual block (R2_Block) module represents the multiscale features at the granularity level and increases the range of the receptive field of each network layer. Furthermore, channel attention module (CAM) and improved position attention module (IPAM) are developed to embed into the DSMPCF branch to learn the geographic feature representation from the DSM images, while the Xception branch is applied to process the IRRG spectral images. Finally, in the fusion part of the proposed model, IPAM and CAM are further utilized to effectively model the fusion features from the spatial and channel dimensions, obtain the class-based correlation, and recalibrate the class-level information. The proposed DSPCANet model is evaluated on the ISPRS Vaihingen and Potsdam datasets, and the extensive experiments demonstrate that it is more accurate and efficient than other state-of-the-art methods.