MSCSA-Net: Multi-Scale Channel Spatial Attention Network for Semantic Segmentation of Remote Sensing Images

Abstract

Although deep learning-based methods for semantic segmentation have achieved prominent performance in the general image domain, semantic segmentation for high-resolution remote sensing images remains highly challenging. One challenge is the large image size. High-resolution remote sensing images can have very high spatial resolution, resulting in images with hundreds of millions of pixels. This makes it difficult for deep learning models to process the images efficiently, as they typically require large amounts of memory and computational resources. Another challenge is the complexity of the objects and scenes in the images. High-resolution remote sensing images often contain a wide variety of objects, such as buildings, roads, trees, and water bodies, with complex shapes and textures. This requires deep learning models to be able to capture a wide range of features and patterns to segment the objects accurately. Moreover, remote sensing images can suffer from various types of noise and distortions, such as atmospheric effects, shadows, and sensor noises, which can also increase difficulty in segmentation tasks. To deal with the aforementioned challenges, we propose a new, mixed deep learning model for semantic segmentation on high-resolution remote sensing images. Our proposed model adopts our newly designed local channel spatial attention, multi-scale attention, and 16-piece local channel spatial attention to effectively extract informative multi-scale features and improve object boundary discrimination. Experimental results with two public benchmark datasets show that our model can indeed improve overall accuracy and compete with several state-of-the-art methods.