Three Dimensional Change Detection Using Point Clouds: A Review
Abstract
:1. Introduction
- Challenges related to the use of point clouds in CD and survey of 3D CD methods;
- Comprehensive review of the popular point clouds datasets used for 3D CD benchmarks;
- Detailed description of evaluation metrics used to quantify change detection performance;
- List of the remaining challenges and future research that will help to advance the development of CD using 3D point clouds.
2. 3D Change Detection Using 3D Point Clouds
2.1. Challenges and Specificities
2.1.1. Acquisition Challenges
2.1.2. Three Dimensional Point Clouds Specificities
2.2. Data Preprocessing
2.3. Three Dimensional Change Detection Methods
- Change Unit. This taxonomy depends on the basic unit used in the CD process, such as methods based on points, voxels, objects and rays;
- Order of classification and change detection. In this categorization, difference exists between methods that proceed to change detection and then classification (pre-classification methods), those that proceed to classification first (post-classification methods), and those that integrate the two steps into one (integrated);
- Used technique. Methods are classified here based on the used technique whether it is based on distance or learning, etc;
- Target. This depends on the application domain: urban, forestry, maritime, etc.
2.3.1. Standard Methods
2.3.2. Machine Learning with Handcrafted Features
2.3.3. Deep Learning Methods
3. Benchmarks
3.1. Datasets for 3D Change Detection
3.2. Evaluation Metrics
4. Discussion and Perspectives
- The use of heterogeneous and multi-modal data (acquired by photogrammetry, laser scanner or other acquisition techniques).
- The use of multi-resolution data (acquired by sensors with different specifications).
- The availability of benchmark data for the 3D CD.
- The exploitation of the progress made in the 3D semantic segmentation to integrate this information in the 3D CD process.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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2D CD | 3D CD | |
---|---|---|
Data source | Optical images, multi spectral images, RADAR images [66], Digital Surface, Terrain, and Canopy Models, and 2D Vector data. | Point clouds, InSAR (Interferometric SAR), Digital surface model, stereo and multi-view images, 3D models, building information models, and RGB-D images. |
Advantages | Well-investigated [7,67,68,69], available datasets [70,71,72,73,74,75], available implementation [68,74,76] | Height component, Robust to illumination differences, Free of perspective effect, and provide volumetric differences. |
Disadvantages | Strongly affected by illumination and atmospheric conditions. Limited by viewpoint and perspective distortions. | Unreliable 3D information may result in artifacts. Limited data availability. Expensive processing. |
Authors | Year | Input Data | Change Detection Approach | Change | ||
---|---|---|---|---|---|---|
LiDAR | Image | Maps | Detection Class | |||
Matikainen et al. [100] | 2004 | X | Ortho | X | Post-classification | Building |
Vu et al. [101] | 2004 | X | Pre-classification | Building | ||
DSM-based | ||||||
Vosselman et al. [102] | 2004 | X | X | Post-classification | Building | |
Choi et al. [103] | 2009 | X | Post-classification | Ground, vegetation, Building | ||
Matikainen et al. [104] | 2010 | X | Ortho | X | Post-classification | Building |
Stal et al. [105] | 2013 | X | Ortho | Post-classification | Building | |
Malpica et al. [106] | 2013 | X | Original | Post-classification | Building | |
Teo et al. [107] | 2013 | X | Post-classification | Building | ||
DSM-based | ||||||
Pang et al. [56] | 2014 | X | Pre-classification | Building | ||
DSM-based | ||||||
Zhang et al. [108] | 2014 | X | Pre-classification | Ground | ||
Tang et al. [109] | 2015 | X | X | Post-classification | Building | |
Awrangjeb et al. [26] | 2015 | X | X | Post-classification | Building | |
Xu et al. [57,110] | 2013, 2015 | X | Post-classification | Building | ||
Xu et al. [57,111] | 2015 | X | Pre-classification | Building, tree | ||
Du et al. [112] | 2016 | X | Original | Pre-classification | Building | |
Matikainen et al. [113] | 2016 | X | Ortho | X | Post-classification | Building |
Matikainen et al. [114] | 2017 | X | Ortho | X | Post-classification | Building, roads |
Kaiguang et al. [115] | 2018 | X | Post-classification | Forest | ||
Marinelli et al. [116] | 2018 | X | Post-classification | Forest | ||
Zhang et al. [117] | 2019 | X | Ortho | Integrated | Building | |
Zhang et al. [118] | 2019 | X | Ortho | Integrated | Building | |
Yrttimaa et al. [22] | 2020 | X | Post-classification | Forest | ||
Fekete et al. [119] | 2021 | X | Post-classification | Tree | ||
DSM-based | ||||||
Huang et al. [120] | 2021 | X | Original | Post-classification | Building | |
Ku et al. [121] | 2021 | X | Integrated | Building, street, tree | ||
Iris et al. [122] | 2021 | X | Integrated | Building | ||
Tran et al. [123] | 2021 | X | Integrated | Ground, vegetation, building | ||
Zhang [124] | 2022 | X | Integrated | Building | ||
Dai et al. [36] | 2022 | X | Integrated | Building |
Dataset | Class Label | Change Label | Years | Reference |
---|---|---|---|---|
OpenTopography | Multiple years | [138,139] | ||
AHN1, AHN2, AHN3, AHN4 | X | Multiple years | [141] | |
Abenberg—ALS test dataset | X | 2008–2009 | [142] | |
4D objects by changes | X | 2017 | [143,144] | |
ICRA 2017—Change Detection Datasets | 2017 | [145] | ||
PLS dataset of Kijkduin beach-dune | 2016–2017 | [146] | ||
CG-PB-M3C2 | 2019 | [147] | ||
Near-continuous 3D time series | [148] | |||
Change3D Benchmark | X | 2016–2020 | [121] | |
TUM City Campus—MLS test dataset | X | 2009–2016–2018 | [150] | |
URB3DCD | X | X | [122] | |
The 2017 Change Detection Dataset | X | 2017 | [152] |
Detected | Reference | |
---|---|---|
Changed | Not Changed | |
Changed | TP | FP |
Not changed | FN | TN |
Metric | Description | Equation |
---|---|---|
Overall accuracy | It is the general evaluation metric for prediction results. | |
Precision | It measures the fraction of detections that were changed. | |
Recall | It measures the fraction of correctly detected changes. | |
F1 score | It refers to recall and precision together. | |
Intersection over union | Or the Jaccard Index. |
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Kharroubi, A.; Poux, F.; Ballouch, Z.; Hajji, R.; Billen, R. Three Dimensional Change Detection Using Point Clouds: A Review. Geomatics 2022, 2, 457-485. https://doi.org/10.3390/geomatics2040025
Kharroubi A, Poux F, Ballouch Z, Hajji R, Billen R. Three Dimensional Change Detection Using Point Clouds: A Review. Geomatics. 2022; 2(4):457-485. https://doi.org/10.3390/geomatics2040025
Chicago/Turabian StyleKharroubi, Abderrazzaq, Florent Poux, Zouhair Ballouch, Rafika Hajji, and Roland Billen. 2022. "Three Dimensional Change Detection Using Point Clouds: A Review" Geomatics 2, no. 4: 457-485. https://doi.org/10.3390/geomatics2040025
APA StyleKharroubi, A., Poux, F., Ballouch, Z., Hajji, R., & Billen, R. (2022). Three Dimensional Change Detection Using Point Clouds: A Review. Geomatics, 2(4), 457-485. https://doi.org/10.3390/geomatics2040025