Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Sentinel-2 satellites, equipped with board-mounted multispectral payloads, provide images of the Earth in the wavelengths of the electromagnetic spectrum. These sensors can be used on Earth's surface to observe it with a very high spatial resolution, and there is a wide range of near-infrared and non-infrared spectral bands. These spectral ranges allow for a wide array of purposes, like assessing vegetation health land use and land cover changes, coastal and inland water quality assessment, and making disaster maps. In this case study, we mapped and analyzed different types of Sentinel-2 images that imaged Kirkuk city landscape in Iraq. The methodology is based on Artificial Intelligence (AI) analyzing patterns of Sentinel hub data and geographic information systems (GIS)-based mapping. Furthermore, minimum noise fraction (MNF) and pixel purity index (PPI) were applied on each image to enhance the spectral unmixing accuracy which provides higher precise endmember spectra. A deep neural remote sensing has been used for 10 m resolution Sentinel-2 image segmentation of Kirkuk. This produces such a result where features are exactly distinguished and identified. This is performed by automatically detecting and classifying features on the image to provide better identification and estimation. Analyzing received data through Sentinel-2 provides us with the basis to identify such patterns, trends, and insights as key facts supporting urban planning, environmental sustainability, and so much more. Received: 25 March 2024 | Revised: 31 May 2024 | Accepted: 23 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Huda Jamal Jumaah: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Aydin Adnan Rashid: Software. Serri Abdul Razzaq Saleh: Validation. Sarah Jamal Jumaah: Investigation.

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Publication Details

Journal
Journal of Optics and Photonics Research
Published
2024-08-29
DOI
https://doi.org/10.47852/bonviewjopr42022920
Citations
15
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
4.79
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article

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Sarah Jamal Jumaah, Huda Jamal Jumaah, Serri Abdul Razzaq Saleh, Aydin Adnan Rashid
15 citations
Journal of Optics and Photonics Research
Remote-Sensing Image Classification
4.79
article

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Sarah Jamal Jumaah, Huda Jamal Jumaah, Serri Abdul Razzaq Saleh, Aydin Adnan Rashid
article en
15 citations

Abstract

Sentinel-2 satellites, equipped with board-mounted multispectral payloads, provide images of the Earth in the wavelengths of the electromagnetic spectrum. These sensors can be used on Earth's surface to observe it with a very high spatial resolution, and there is a wide range of near-infrared and non-infrared spectral bands. These spectral ranges allow for a wide array of purposes, like assessing vegetation health land use and land cover changes, coastal and inland water quality assessment, and making disaster maps. In this case study, we mapped and analyzed different types of Sentinel-2 images that imaged Kirkuk city landscape in Iraq. The methodology is based on Artificial Intelligence (AI) analyzing patterns of Sentinel hub data and geographic information systems (GIS)-based mapping. Furthermore, minimum noise fraction (MNF) and pixel purity index (PPI) were applied on each image to enhance the spectral unmixing accuracy which provides higher precise endmember spectra. A deep neural remote sensing has been used for 10 m resolution Sentinel-2 image segmentation of Kirkuk. This produces such a result where features are exactly distinguished and identified. This is performed by automatically detecting and classifying features on the image to provide better identification and estimation. Analyzing received data through Sentinel-2 provides us with the basis to identify such patterns, trends, and insights as key facts supporting urban planning, environmental sustainability, and so much more. Received: 25 March 2024 | Revised: 31 May 2024 | Accepted: 23 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Huda Jamal Jumaah: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Aydin Adnan Rashid: Software. Serri Abdul Razzaq Saleh: Validation. Sarah Jamal Jumaah: Investigation.

Journal of Optics and Photonics ResearchVol. 2(3)
University of Kirkuk (IQ), Northern Technical University (IQ)
Openalex Percentile: Top 5%
Remote-Sensing Image Classification
4.79
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