An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

Abstract

Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object-and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.

Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

“…In terms of geometric comparison, some scholars proposed detecting building changes with height differencing [11][12][13][14] and projection-based differences [15]. In such methods, DSM is generally derived from an airborne laser scanner (ALS).…”

“…Quality =TP + TN TP + FN + FP1 + FP + TN(14) Confusion matrix with different change types, where yellow represents true positive (TP), blue represents false negative (FN), rose and brown represent two types of false positive (e.g., FP1 and FP), and white represents true negative (TN).…”

Editorial for Special Issue: “Remote Sensing based Building Extraction”

“…Therefore, intelligent and innovative algorithms are in dire need for high success of automatic building extraction and modelling. This Special Issue focuses on the newly-developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D roof modelling.In the Special Issue, the published papers cover a wide range of related topics including building detection [3], boundary extraction [4] and regularization [5], 3D indoor space (room) modelling [6], land cover classification [7], building height model extraction [8], 3D roof modelling [6,9] and change detection [9].In terms of datasets, some of the published works use publicly available benchmark datasets, e.g., ISPRS (International Society for Photogrammetry and Remote Sensing) urban object extraction and modelling datasets [4,5,10]; ISPRS 2D semantic labelling datasets [1]; Inria aerial image labelling benchmark datasets [11][12][13]; and IEEE (Institute of Electrical and Electronics Engineers) DeepGlobe Satellite Challenge datasets [14].The proposed methods fall into two main categories depending the use of the input data sources: Methods based on single source data, and methods that use multi-source data. Methods based on single source data can use point cloud data [9], aerial imagery [4] and digital surface models (DSM) [8].…”

“…In terms of datasets, some of the published works use publicly available benchmark datasets, e.g., ISPRS (International Society for Photogrammetry and Remote Sensing) urban object extraction and modelling datasets [4,5,10]; ISPRS 2D semantic labelling datasets [1]; Inria aerial image labelling benchmark datasets [11][12][13]; and IEEE (Institute of Electrical and Electronics Engineers) DeepGlobe Satellite Challenge datasets [14].…”

“…This method offers better results than the top three solutions in the SpaceNet building detection competition. Dong et al [10] present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching point clouds, orthophoto and original aerial images. Song et al [7] present a comparative study on image fusion methods, that achieves the complementarity information of the panchromatic band and multispectral bands in high spatial resolution remote sensing images.…”