Progressive Refinement Learning Based on Feature Cross Perception for Residential Areas Semantic Segmentation

Abstract

Due to the pixel-level accurate annotation of remote sensing images consumes a lot of labor costs, weak annotation semantic segmentation has become a hotspot in recent years. However, due to the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for residential areas by progressive refinement learning. This method mainly consists of two parts. In the first part, we constructed a classification network and proposed an initial pixel-level label calculation method based on multi-layer category feature awareness. In the second part, we proposed to construct feature cross perceptron module in the structure of the multi-level codec to achieve image pair semantic co-segmentation. In addition, we used confidence maps to modify the loss function to achieve more accurate results. Comprehensive evaluations with GeoEye-1 dataset and the comparison with 7 methods validate the superiority of the proposed model.