Satellite On-Board Change Detection via Auto-Associative Neural Networks
Open accessAbstract
The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique.
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AANN is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction.
“The program is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction [261,262].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [261]
The Introduction (page not supplied) says AANNs have been used for dimensionality reduction and feature extraction from remote-sensing images. The Conclusions describe this paper's use of extracted features for change detection. Our copy does not describe AANNs as simulating nonlinear PCA or identify pattern recognition as an application. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAANN is an ANN structure with the same input and output layer.
“AANN is an ANN structure with the same input and output layer.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [261]
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- Authors
- Guerrisi, G., Del Frate, F., & Schiavon, G.
- Affiliations
- University of Rome Tor Vergata (IT)
- Department of Civil Engineering and Computer Science Engineering, “Tor Vergata” University of Rome, 00133 Rome, Italy
- Corresponding author
- Guerrisi, Giorgia
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2022-06-07
- Volume
- 14
- Issue
- 12
- Pages
- 2735
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4282551395
- Cited by
- 8
- References
- 33
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 5
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- autoencoder, satellite imagery, feature extraction, image dissimilarity, data compression, Sentinel-2, Earth observation
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