Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image

Abstract

Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies are very easy to be confused with the spectra of dark objects such as building shadows, asphalt roads and dense vegetation. The existing semantic segmentation methods do not pay enough attention to the local feature information between horizontal direction and position, which leads to the problem of misjudgment of buildings and loss of local information of water area. In order to improve this problem, this paper proposes a local feature search network (DFSNet) application in remote sensing image building and water segmentation. By paying more attention to the local feature information between horizontal direction and position, we can reduce the problems of misjudgment of buildings and loss of local information of water bodies. The discarding attention module (DAM) introduced in this paper reads sensitive information through direction and location, and proposes the slice pooling module (SPM) to obtain a large receptive field in the pixel by pixel prediction task through parallel pooling operation, so as to reduce the misjudgment of large areas of buildings and the edge blurring in the process of water body segmentation. The fusion attention up sampling module (FAUM) guides the backbone network to obtain local information between horizontal directions and positions in spatial dimensions, provide better pixel level attention for high-level feature maps, and obtain more detailed segmentation output. The experimental results of our method on building and water data sets show that compared with the existing classical semantic segmentation model, the proposed method achieves 2.89% improvement on the indicator MIoU, and the final MIoU reaches 83.73%.