Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

Tuna, Caglayan; Merciol, François; Lefèvre, Sébastien

doi:10.1109/jurse.2019.8808971

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Academic tasks are more complex than modeling a cycle laser field, especially on a dynamic time-scale spatial database [6]. Monitoring changes in urban growth is an essential issue in urban planning and disaster management [7]. Earth images are enormous and complicated because they have been selected as a neural network dedicated to image enhancement, a specific location with no place to detect and improve it [8] automatically.…”

Section: Literature Surveymentioning

confidence: 99%

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

Mansour¹,

Alabdulkreem²

2023

Computer Systems Science and Engineering

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The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).

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Section: Literature Surveymentioning

confidence: 99%

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

Mansour¹,

Alabdulkreem²

2023

Computer Systems Science and Engineering

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“…According to these results, SH is not adequate to classify SITS, but on the other hand, it can be used for other applications. For example, in our previous work (Tuna et al, 2019), we proposed a SH approach for change detection, through filtering tree with a projected SITS. 1636, 133374, 2010, 226231 and5354…”

Section: Classificationmentioning

confidence: 99%

Component trees for image sequences and streams

Tuna

Mirmahboub

Merciol

et al. 2020

Pattern Recognition Letters

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Falco et al perform pixelwise change detection [8] comparing sums of differential attribute profiles (AP) in each pixel. Recently, monitoring urban growth was achieved in [9] where spatial area attributes of the SITS are derived from hierarchical representations. However, and to the best of the authors' knowledge, such hierarchical representations were never used for flood detection yet.…”

Section: Introductionmentioning

confidence: 99%