Integration of remote sensing data with the field and laboratory investigation for lithological mapping of granitic phases: Kadabora pluton, Eastern Desert, Egypt

2008 ◽  
Vol 2 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Ahmed Mohamed Youssef ◽  
Asran Mohamed Hassan ◽  
Mohamed Abd El Moneim Mohamed
Keyword(s):  
Remote Sensing ◽  
Laboratory Investigation ◽  
Remote Sensing Data ◽  
Eastern Desert ◽  
Lithological Mapping ◽  
Sensing Data

scholarly journals Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone, south Iran

2015 ◽  
Vol 65 (4) ◽  
pp. 375-388 ◽  
Author(s):  
Alireza Eslami ◽  
Majid Ghaderi ◽  
Sankaran Rajendran ◽  
Amin Beiranvand Pour ◽  
Mazlan Hashim
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landsat Tm ◽  
Lithological Mapping ◽  
Sensing Data

scholarly journals GEO-ENVIRONMENTAL STUDIES ON WADI QENA, EASTERN DESERT, EGYPT. BY USING REMOTE SENSING DATA AND GIS.

2011 ◽  
Vol 22 (Issue 2-D) ◽  
pp. 33-60 ◽  
Author(s):  
E. K. EL-SAWY ◽  
M. H. BEKHIET ◽  
ABD EL-MOTAAL, E. ◽  
A. A. ORABI
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Environmental Studies ◽  
Eastern Desert ◽  
Sensing Data ◽  
Wadi Qena

scholarly journals Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

2019 ◽  
Vol 8 (6) ◽  
pp. 248 ◽  
Author(s):  
Imane Bachri ◽  
Mustapha Hakdaoui ◽  
Mohammed Raji ◽  
Ana Cláudia Teodoro ◽  
Abdelmajid Benbouziane
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

Lithologic Mapping using Remote Sensing Data in Abu Marawat Area, Eastern Desert of Egypt

2017 ◽  
Vol 6 (1) ◽  
pp. 2171-2177 ◽  
Author(s):  
Mohamed Abdelkareem ◽  
◽  
Ibrahim Othman ◽  
Kamal El Din G ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Eastern Desert ◽  
Sensing Data ◽  
Lithologic Mapping
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