An investigation in satellite images based on image enhancement techniques

Ablin, R.; Sulochana, C. Helen; Prabin, G.

doi:10.1080/22797254.2019.1673216

Cited by 21 publications

(11 citation statements)

References 63 publications

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“…Enhancement consists of processing an image so that the result is more appropriate than the original image for a specific remote sensing application. Satellite image enhancement techniques offer many choices for improving the visual quality of remote sensing images (Ablin et al, 2020).…”

Section: Image Enhancementmentioning

confidence: 99%

Contribution of ALOS PALSAR DEM Imagery to the Study of Geological and Morphostructural Lineaments of Boma Trough Block, Onshore Coastal Basin of D.R. Congo

Diambu,

Osomba,

Mayena

et al. 2023

Idn. J. Earth Sci.

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Geological and morphostructural lineaments are considered as the surface expression of geological structures and linear features of valleys, ridges and river drainage systems. Lineaments extracted from satellite images have always been effective in understanding the structural context of a region. The lineaments observed on the surface provide information on the tectonic stresses that have affected an area and give a first impression of the probable existence and orientation of geological structures that may constitute oil traps at depth. This study aims to semi-automatically extract lineaments (by combining the use of GIS software, remote sensing and operator intervention) in Boma Trough Block, an oil block located to the east of D.R. Congo Coastal Basin and still poorly explored. The satellite image used in this work is an ALOS PALSAR DEM with a spatial resolution of 12.5m, enhanced in traditional hillshade (solar azimuth of 315° and 45°) and multidirectional hillshade; which allowed the extraction of 3129 lineaments, with lengths ranging from 0.16 to 3.79 km, oriented on multidirectional hillshade at 47.1% along the NW-SE direction, at 44.1% along the NE-SW, 7.3% for N-S and 1.4% for E-W. Lineament density mapping revealed that areas with high and very high densities cover 43% of ??the study area, where the Precambrian basement is largely outcropping, while low and very low density areas represent 41% and contain sedimentary formations. Areas with moderate densities covered ??16%. The lineaments extracted from the DEM image compared to the reality on the field show a positive correlation. This confirms the important contribution of the processing approach used in this study.

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Section: Image Enhancementmentioning

confidence: 99%

Contribution of ALOS PALSAR DEM Imagery to the Study of Geological and Morphostructural Lineaments of Boma Trough Block, Onshore Coastal Basin of D.R. Congo

Diambu,

Osomba,

Mayena

et al. 2023

Idn. J. Earth Sci.

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“…Satellite image classification is utilized to extract spectral signature from the satellite images as well as analyzing the land-cover map of a selected area [7][8] [9]. The techniques of classification can be categorized into 2 types: supervised and unsupervised [10] [11]. The work aims to study and measure the wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia in year (2019).…”

Section: Introductionmentioning

confidence: 99%

Classification and Measurement of Land Cover of Wildfires in Australia Using Remote Sensing

Hassan

Saeed

Al-Ghrairi

2022

eijs

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Remote sensing techniques used in many studies for classfying and measuring of wildfires. Satellite Landsat8(OLI) imagery is used in the presented work. The satellite is considered as a near-polar orbit, with a high multispectral resolution for covering Wollemi National Park in Australia. The work aims to study and measure wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia for a year (October, 2019), as well as analyzing the harm resulting from such wildfires and their effects on earth and environment through recognizing satellite images for studied region prior to and throughout wildfires. A discussion of methods for computing the affecred area is covered regarding each one of the classes and lessening or limiting the quickly-spreading wildfires damage. This paper propose a 2-phases techniques: training and classifying. In the training phase, the number of clustering is computed by using C# Programming Language and feature extracted and clustered as a group and stored in the dataset. The classification used the moments with (K-Means) classification approach in RS (Remote Sensing) for classified image. The results of classification showed 5 distinctive classes (trees, rivers, bare earth, buildings with no trees, and buildings with trees) in which it might be indicates that the region is secured via each one of the classes prior to and throughout wildfires as well as the changed pixels with regard to all the classes. Also, the classification experimental methods results indicate an excellent performance recision with a good classifying and result analysis about the harms caused by fires in the study area.

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“…Wherever, the classes are recognized to a characterized topical class (water, trees, building without trees, buildings with trees, and bare lands) [3][4][5]. Satellite image categorizing is broadly utilized for extracting the spectral highlights from satellite images and analyze land-cover map of the area selected [6][7][8]. Where, the categorizing techniques can be defined as two significant types: unsupervised and supervised categorizing [9,10].…”

Section: Introductionmentioning

confidence: 99%

Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing

Mohammed¹,

Khamees

2021

IJEECS

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This paper has been utilized satellite Sentinel-2A imagery, this satellite is a polar-orbiting, multispectral high-resolution to cover Athens city, Greece that located at latitude (37° 58′ 46″) N, (23° 42′ 58″) E.,the work aims to measurement and study the wildfires natural resourcesbefore and after fire break out that happenedin forests of Athens city in Greece for a year (2007, 2018) and analysis the damage caused by these wildfiresand their impact on environment and soil by categorize the satellite images for the interested region before and after wildfires for a year (2007) and a year (2018) and Discuss techniques that compute the area covered of each class and lessen or limit the rapidly spreading wildfires damage.The categorizing utilizing the moments with (K-Means) grouping algorithm in RS (remote sensing). And the categorizing results show five unique classes (water, trees, buildings without tree, buildings with tree, bare lands) where, it can be notice that the region secured by each class before and after wildfires and the changed pixels for all classes.The experimental resulted of categorizing technique shows that the good performance exactness with a good categorizing and result analysisa bout the harms resulted from the fires in the forest Greece for a years (2007 and 2018).

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An investigation in satellite images based on image enhancement techniques

Cited by 21 publications

References 63 publications

Contribution of ALOS PALSAR DEM Imagery to the Study of Geological and Morphostructural Lineaments of Boma Trough Block, Onshore Coastal Basin of D.R. Congo

Contribution of ALOS PALSAR DEM Imagery to the Study of Geological and Morphostructural Lineaments of Boma Trough Block, Onshore Coastal Basin of D.R. Congo

Classification and Measurement of Land Cover of Wildfires in Australia Using Remote Sensing

Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing

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