A critical synthesis of remotely sensed optical image change detection techniques

By A. Tewkesbury, Alexis J. Comber, Nicholas J. Tate, Alistair Lamb, Peter F. Fisher. 2015

496 citations recorded by the source corpus.

State of the art reviews of remote sensing change detection are becoming increasingly complicated and disparate due to an ever growing list of techniques, algorithms and methods. To provide a clearer, synoptic view of the field this review has organised the literature by the unit of analysis and the comparison method used to identify change. This significantly reduces the conceptual overlap present in previous reviews giving a succinct nomenclature with which to understand and apply change detection workflows. Under this framework, several decades of research have been summarised to provide an overview of current change detection approaches. Seven units of analysis and six comparison methods were identified and described highlighting the advantages and limitations of each within a change detection workflow. Of these, the pixel and post-classification change methods remain the most popular choices. In this review we extend previous summaries and provide an accessible description of the field. This supports future research by placing a clear separation between the analysis unit and the change classification method. This separation is then discussed, providing guidance for applied change detection research and future benchmarking experiments.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/15bc476f6811fbfb9e6849cee0d2c029024677c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Qian Du
2. Lorenzo Bruzzone
3. Le Wang
4. Qi Wang
5. Zhenwei Shi
6. Yongjun Zhang
7. Daifeng Peng
8. Haiyan Guan

In Ecology

1. Qian Du
2. L. Jiao
3. B. Cooke
4. Rob S. Skakun
5. R. Hall
6. Guillermo Castilla
7. Qi Wang
8. Lorenzo Bruzzone

In Media Technology

1. Qian Du
2. Xuelong Li
3. Qi Wang
4. Zhenwei Shi
5. Yongjun Zhang
6. Le Wang
7. Lorenzo Bruzzone
8. Daifeng Peng

Citations in the corpus

• Deep learning for change detection in remote sensing: a review (2022, methodology): “However, the extraction accuracy is heavily dependent on the accuracy of the initial segmentation (Cao et al. 2014; Tewkesbury et al. 2015; Hussain et al. 2013).”
• SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, background): “An extensive summary and reviews of CD algorithms have been made in [5]–[7], where it has been shown that CD techniques evolved with the development of machine learning and pattern recognition.”
• Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “As a result, HSIs may detect finer variations [80], reflecting the exact composition of distinct objects.”
• Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, unknown): “[144] provides a review of these approaches.”
• Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “This approach, also called layer arithmetic, is among the simplest and most frequently used method of bitemporal change detection (Lu et al. 2004; Tewkesbury et al. 2015).”
• Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, methodology): “Image arithmetical operation is the common approach including image subtraction, image ratioing, and image regression, etc. [9,11,12].”
• A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (2020, methodology): “Automated CD technology has facilitated the development of remote sensing applications and has been drawing extensive attention in recent years [8].”
• GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection (2019, background): “Thus, HSIs have the potential to identify finer changes [6], with the detailed composition of different objects being reflected.”
• Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, background): “Existing change detection reviews have focused mainly on the design of change detection techniques in multi-temporal hyperspectral images (HSIs) and high-spatial-resolution images [1-4].”
• Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, background): “More detailed reviews in change detection techniques can be found in References [127,133].”