Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis
1.1. Related Concepts and Issues in Change Detection
1.1.1. Nature of Changes
- Data acquisition: Positional discrepancies could be the result of varying accuracy, resolution, and so on in data capture. Some objects might be deliberately omitted during data acquisition for certain purposes.
- Data specifications: By comparison with high-resolution images, we found that OSM buildings with greater details were outlined by including the main building and annexes such as fences, courtyards, and garages, whereas in topographic datasets, mainly building roofs were recorded (Figure 1).
- Generalization (or LOD): Objects can be simplified, displaced, aggregated, or typified during generalization, which creates apparent discrepancies between data representations.
- Physical and nominal changes: The discrepancies due to real world changes to the construction itself (e.g., new construction, removal, and partial rebuilt) or to its semantics (e.g., land-use type, name, etc.)
1.1.2. Previous Work and Issues in Change Detection
2.1. Overall Process
- Identifying corresponding objects between two datasets (Section 2.2). We aim to identify objects (or a group of objects) in the two datasets that correspond to each other, which is a prerequisite for the subsequent analysis.
- Rule-based change detection (Section 2.3). During this stage, change detection is carried out at the individual level (i.e., building footprints) using different rules and analysis proposed in this paper.
- Refining results with patterns and contextual information (Section 2.4). In this stage, we show how inconsistent results in change detection can be corrected using the building pattern constraint.
2.2. Object Matching
2.3. Rules for Change Detection
- Absolute and relative size of the differences.
- Set-based similarity: For any two overlapping polygons A and B, three basic sets can be distinguished: A-B, B-A, and A∩B (Figure 1). At the implementation level, A and B result in three non-overlapping polygons, some of which may contain multiple parts (Figure 1b). Basic geometric measures and advanced morphological analysis are performed on these parts.
- Shape-based analysis  for measuring building similarity and characterizing the overall shape and difference parts.
2.3.1. Aggregation with Minimum Cost for the Many-to-Many Correspondence
2.3.2. Controlled Alignment of Corresponding Objects
2.3.3. Global Shape Similarity Using Turning Function
2.3.4. Morphological Analysis of Difference Parts
- For small buildings, absolute and relative size of the difference should be considered.
- For large buildings, absolute size of the difference is used as a first criterion, and if the size of the difference exceeds Tsize_diff_abs,
- First check if the part can be segmented into multiple smaller pieces (Figure 7d). If any of them exceeds Tsize_diff_abs, proceed with the analysis in sub-step b; if none of them is large enough, the building is regarded as unchanged;
- For any significant part (or segmented piece), quantify their shape by examining if it is long and narrow, thin belt-shaped (not changed), or in a more compact form (changed).
2.3.5. Rules and Parameters
2.4. Correcting Detected Changes with Pattern and Contextual Information
- Compute Delaunay triangulation (DT) on the data area.
- Derive the proximity graph of building footprints, ProxG〈V, E〉, where V is the set of buildings and E is the set of building pairs connected by at least a triangle.
- Prune any edge in ProxG if its two connecting buildings are very different in size, shape, and orientation.
- Trace alignments in the pruned ProxG following the criteria in Zhang et al.  and characterized by their homogeneity value (i.e., significance).
3. Experiment Design and Results
3.1. Data Description and Evaluation Methods
3.2. Detected Changes
3.2.1. General Results
3.2.2. Effectiveness of the Chosen Rules and Parameters
3.2.3. Corrections Guided by Building Patterns
4.1. Uncertainty and User Parameters
4.2. Effect of Scales on Change Detection
4.3. Potential Use of Contextual Information
4.4. Fit into Machine Learning?
Conflicts of Interest
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Zhou, X.; Chen, Z.; Zhang, X.; Ai, T. Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis. ISPRS Int. J. Geo-Inf. 2018, 7, 406. https://doi.org/10.3390/ijgi7100406
Zhou X, Chen Z, Zhang X, Ai T. Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis. ISPRS International Journal of Geo-Information. 2018; 7(10):406. https://doi.org/10.3390/ijgi7100406Chicago/Turabian Style
Zhou, Xiaodong, Zhe Chen, Xiang Zhang, and Tinghua Ai. 2018. "Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis" ISPRS International Journal of Geo-Information 7, no. 10: 406. https://doi.org/10.3390/ijgi7100406