Infrastructure monitoring using SAR and multispectral multitemporal images

Datta, Urmila

doi:10.1117/12.2573894

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In a wide range of applications, change detection using multitemporal remote sensing imaging is crucial. Examples include urbanization [ 26 ], deforestation [ 27 ], flooding [ 28 ], infrastructure monitoring [ 29 ], disaster monitoring, and damage assessment [ 30 ]. Reference [ 14 ], however, there are only a few publications on the use of SAR image change detection in the military and security—here, an example can be given Novak [ 16 ] or Canty [ 31 ].…”

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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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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“…The ability to identify assets across multiple CI sectors is particularly important. Doing so enables identification of not only individual assets but also inferences to be made of functional relationships between assets in different sectors across both service provision and geographic infrastructural interdependencies [5]. An additional challenge associated with the study of CI is the evolving nature of CI systems.…”

Section: Introductionmentioning

confidence: 99%

Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

et al. 2022

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To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types.

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Multimodal change monitoring using multitemporal satellite images.

Datta

2021

Image and Signal Processing for Remote Sensing XXVII

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Infrastructure monitoring using SAR and multispectral multitemporal images

Cited by 3 publications

References 11 publications

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

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

Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

Multimodal change monitoring using multitemporal satellite images.

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