RSFNet: a method for remote sensing image semantic segmentation based on fully convolutional neural networks

Abstract

The advancement of remote sensing technology has broadened the application scope of remote sensing image data across various fields. Traditional methods, when processing remote sensing images, face limitations in efficiency and generalization capabilities due to their intricate geographical features. In contrast, deep learning segmentation methods exhibit superior performance but struggle with contextual detail loss and multi-scale features. In this paper, we introduce the RSFNet model to tackle these issues. The model employs spatial paths to extract detailed information from low-level features, presents a residual ASPP incorporating an attention mechanism, and utilizes a feature map slicing module to capture small target features. Experimental results show that RSFNet attains 88.38% pixel accuracy (PA) and 81.06% mean intersection over union (mIoU) on the Potsdam dataset, proving its suitability for semantic segmentation of remote sensing images.