Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation

Abstract

Remote sensing image segmentation is a challenging task in remote sensing image analysis. Remote sensing image segmentation has great significance in urban planning, crop planting, and other fields that need plentiful information about the land. Technically, this task suffers from the ultra-high resolution, large shooting angle, and feature complexity of the remote sensing images. To address these issues, we propose a deep learning-based network called ATD-LinkNet with several customized modules. Specifically, we propose a replaceable module named AT block using multi-scale convolution and attention mechanism as the building block in ATD-LinkNet. AT block fuses different scale features and effectively utilizes the abundant spatial and semantic information in remote sensing images. To refine the nonlinear boundaries of internal objects in remote sensing images, we adopt the dense upsampling convolution in the decoder part of ATD-LinkNet. Experimentally, we enforce sufficient comparative experiments on two public remote sensing datasets (Potsdam and DeepGlobe Road Extraction). The results show our ATD-LinkNet achieves better performance against most state-of-the-art networks. We obtain 89.0% for pixel-level accuracy in the Potsdam dataset and 62.68% for mean Intersection over Union in the DeepGlobe Road Extraction dataset.