Efficient Rural Building Segmentation via a Multilevel Decoding Network

This article addresses the problem of building segmentation for rural areas with high-resolution remote sensing images. Due to the irregular spatial distribution of rural buildings, it is often challenging to perform pixel-wise dense prediction to entire areas like the usual segmentation task to extract buildings. Specifically, we present a multilevel decoding network model that classifies the input image on the patch and image levels according to the distribution of buildings. A scene head module is used to identify scenes defined as patches that contain buildings. Depending on the scene classification results, a decode gate is taken to determine the level of prediction. This hierarchical extraction strategy reduces the amount of inference time. Experiments on our constructed rural building dataset with large-scale images validate the high efficiency of the proposed method.