Multi-Scale Building Instance Extraction Framework in High Resolution Remote Sensing Imagery Based on Feature Pyramid Object-Aware Convolution Neural Network

Abstract

Building extraction based on high-resolution remote sensing imagery has been widely used in automatic surveying and mapping. Recently, the instance segmentation algorithm has been introduced to the building extraction, which can calculate the number and area of buildings simultaneously. However, there are some challenges: 1) multi-scale buildings; 2) occlusion by other adjacent buildings. In this paper, to solve these problems, we propose a multi-scale building instance extraction framework based on feature pyramid object-aware convolution neural network (CNN). In order to solve the multi-scale problem, a feature pyramid CNN is proposed, which combines features from both the bottom-up and top-down architectures. In order to solve the occlusion problem, a multi-scale object-aware instance proposal network is proposed, which introduces the multiscale attention mechanism to aware objects. The experiments conducted on two public datasets and a self-constructed dataset of Changzhou show that the proposed method can achieve an excellent performance.