Hadeer Ahmed Desoky , Mohamed Abd El-Dayem , Mahmoud Abd El-Rahman Hegab
{"title":"利用 Landsat-8、Sentinel-2B 和 Sentinel-1A 卫星图像评估线状提取效率的比较分析:埃及苏伊士运河区周边案例研究","authors":"Hadeer Ahmed Desoky , Mohamed Abd El-Dayem , Mahmoud Abd El-Rahman Hegab","doi":"10.1016/j.rsase.2024.101312","DOIUrl":null,"url":null,"abstract":"<div><p>Satellite remote sensing data has been extensively utilized in various fields, for example topography, geology, and hydrogeology, to extract lineament information. With notable advancements in remote sensing techniques, the process of lineament extraction and identification can now be performed in a more efficient and accurate manner, surpassing traditional manual methods. This study presents a comparative analysis utilizing Landsat-8, Sentinel-2B, and Sentinel-1A data to automatically extract lineaments. The approach includes ground truth data, an existing geological map, and a Digital Elevation Model (DEM) in addition to the data on satellite images. Through the use of a semi-totally automatic method that combines a line-linking algorithm and an edge-line detection technique, within the study area, we have determined the optimal parameters for automated lineament extraction. It has been demonstrated through further comparison and assessment of the data that using Sentinel-1A data resulted in more efficient restitution of lineaments. This demonstrates how well radar data performs in this kind of investigation when compared to optical data.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101312"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparative analysis to assess the efficiency of lineament extraction utilizing satellite imagery from Landsat-8, Sentinel-2B, and Sentinel-1A: A case study around suez canal zone, Egypt\",\"authors\":\"Hadeer Ahmed Desoky , Mohamed Abd El-Dayem , Mahmoud Abd El-Rahman Hegab\",\"doi\":\"10.1016/j.rsase.2024.101312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Satellite remote sensing data has been extensively utilized in various fields, for example topography, geology, and hydrogeology, to extract lineament information. With notable advancements in remote sensing techniques, the process of lineament extraction and identification can now be performed in a more efficient and accurate manner, surpassing traditional manual methods. This study presents a comparative analysis utilizing Landsat-8, Sentinel-2B, and Sentinel-1A data to automatically extract lineaments. The approach includes ground truth data, an existing geological map, and a Digital Elevation Model (DEM) in addition to the data on satellite images. Through the use of a semi-totally automatic method that combines a line-linking algorithm and an edge-line detection technique, within the study area, we have determined the optimal parameters for automated lineament extraction. It has been demonstrated through further comparison and assessment of the data that using Sentinel-1A data resulted in more efficient restitution of lineaments. This demonstrates how well radar data performs in this kind of investigation when compared to optical data.</p></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"36 \",\"pages\":\"Article 101312\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352938524001769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938524001769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
A comparative analysis to assess the efficiency of lineament extraction utilizing satellite imagery from Landsat-8, Sentinel-2B, and Sentinel-1A: A case study around suez canal zone, Egypt
Satellite remote sensing data has been extensively utilized in various fields, for example topography, geology, and hydrogeology, to extract lineament information. With notable advancements in remote sensing techniques, the process of lineament extraction and identification can now be performed in a more efficient and accurate manner, surpassing traditional manual methods. This study presents a comparative analysis utilizing Landsat-8, Sentinel-2B, and Sentinel-1A data to automatically extract lineaments. The approach includes ground truth data, an existing geological map, and a Digital Elevation Model (DEM) in addition to the data on satellite images. Through the use of a semi-totally automatic method that combines a line-linking algorithm and an edge-line detection technique, within the study area, we have determined the optimal parameters for automated lineament extraction. It has been demonstrated through further comparison and assessment of the data that using Sentinel-1A data resulted in more efficient restitution of lineaments. This demonstrates how well radar data performs in this kind of investigation when compared to optical data.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems