Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images

Tong Wu,Ling Peng,Ruonan Chen,Yuan Hu

doi:10.3390/rs12182910

Abstract

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed.

Highlights

In pace with the high-speed development of high-resolution remote sensing data in both China and International Community, the spatial information, geometric structures, textural features and intensity information contained in remote sensing images and point cloud data are becoming clearer, which makes it possible to identify and detect terrestrial objects
Awrangjeb et al [10], designed an innovative image line guided segmentation technique to extract the roof planes based on light detection and ranging (LIDAR) and orthoimage, and applied a newly proposed rule-based procedure to removing planes constructed on trees
In this paper, we propose an improved anchor-free instance segmentation method based on CenterMask [37] with spatial and channel attention-guided mechanisms for accurate extraction of buildings in high-resolution remote sensing images

Summary

Introduction

In pace with the high-speed development of high-resolution remote sensing data in both China and International Community, the spatial information, geometric structures, textural features and intensity information contained in remote sensing images and point cloud data are becoming clearer, which makes it possible to identify and detect terrestrial objects. The geometry, area, or the dimensions of buildings gained from two-dimensional information-rich optical remote sensing images and three-dimensional information-containing point cloud data [2] are the relevant urban metrics They can effectively represent the urban spatial structure, and quantify the morphology of city [3], reflect the processes that occur during a city’s development [4], and monitor urban management and planning strategies [5]. Gilani et al [12] used LIDAR data to present a non-manifold points creation methods that provides a better interpolation of roof regions, these geometric features were preserved to achieve automated identification and segmentation of the building roof. The features of buildings used in all the above methods are artificially designed shallow features, which are time-consuming and labor-intensive, sparse for feature distribution, and cannot express higher-level semantic information

Methods

Results

Conclusion