An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

Chenguang Dai,Dong Lin,Zhenchao Zhang

doi:10.3390/rs12101680

Chenguang Dai, Dong Lin + Show 1 more

Open Access

https://doi.org/10.3390/rs12101680

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Abstract

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

Highlights

Object extraction and change detection are two of the most important tasks in remote sensing [1,2]
Some roofs are represented by a complete segment, while some roofs are broken into several sub-segments
We propose an object-based bidirectional method for integrated building extraction and change detection

Summary

Introduction

Object extraction and change detection are two of the most important tasks in remote sensing [1,2]. Object extraction derives topographic information from one single epoch, whereas change detection compares remote sensing data from two epochs to derive change information. ALS point clouds are more accurate than DIM point clouds in terms of vertical accuracy. The former usually contains less noise than the latter. DIM provides, geometric information in point clouds or DSM, and spectral information in the orthoimage. Previous work suggests that the spectral information from orthoimage is complementary to the geometric information for object extraction or change detection tasks [9,10,11]. Even though the accuracy and noise level in DIM data are less satisfying than those of ALS data, the spectral information can fill the gap to some extent

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