SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis

Li, Weisong; Ma, Peifeng; Wang, Haipeng; Fang, Chaoyang

doi:10.1109/tgrs.2023.3243900

Cited by 11 publications

(2 citation statements)

References 58 publications

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“…Then, the neural network was fine-tuned with the labeled samples. A synthetic sample generation model was proposed in [28] to mitigate the need for more real training data for change detection in long-time series SAR images. It is worth noting that images captured in the aftermath of a large earthquake often contain a large number of unlabeled samples, which may serve as an essential source of information and potentially reduce the heavy dependence on manually labeled data.…”

Section: Introductionmentioning

confidence: 99%

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Portillo,

Moya

2023

Remote Sensing

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Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake.

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Section: Introductionmentioning

confidence: 99%

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Portillo,

Moya

2023

Remote Sensing

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“…Although there are novel visualization models, such as REACTIV algorithm, simultaneously incorporating change frequency and change time, it is still lacking a description of change patterns. A novel unified framework for long-time series SAR image change detection and change pattern analysis (SAR-TSCC) was proposed for land cover change mapping [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

Slesinski,

Wierzbicki,

Kedzierski

2023

Sensors

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Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.

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Understanding Satellite Image Processing and Black Box Models with Ante-Hoc and Post-Hoc Explanations in Deep Learning

Kasetty,

Rajakumar

2024

2024 10th International Conference on Communication and Signal Processing (ICCSP)

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SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis

Cited by 11 publications

References 58 publications

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

Understanding Satellite Image Processing and Black Box Models with Ante-Hoc and Post-Hoc Explanations in Deep Learning

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