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Ghasem Askari

Assistant Professor of GIS and Remote Sensing

Selected Publications

Askari, G., Pour, A.B., Pradhan, B., Sarfi, M., Nazemnejad, F. Band ratios matrix transformation (BRMT): A sedimentary lithology mapping approach using ASTER satellite sensor (2018) Sensors (Switzerland), 18 (10), art. no. 3213, .

DOI: 10.3390/s18103213

Remote sensing imagery has become an operative and applicable tool for the preparation of geological maps by reducing the costs and increasing the precision. In this study, ASTER satellite remote sensing data were used to extract lithological information of Deh-Molla sedimentary succession, which is located in the southwest of Shahrood city, Semnan Province, North Iran. A robust and effective approach named Band Ratio Matrix Transformation (BRMT) was developed to characterize and discriminate the boundary of sedimentary rock formations in Deh-Molla region. The analysis was based on the forward and continuous division of the visible-near infrared (VNIR) and the shortwave infrared (SWIR) spectral bands of ASTER with subsequent application of principal component analysis (PCA) for producing new transform datasets. The approach was implemented to ASTER spectral band ratios for mapping dominated mineral assemblages in the study area. Quartz, carbonate, and Al, Fe, Mg –OH bearing-altered minerals such as kaolinite, alunite, chlorite and mica were appropriately mapped using the BRMT approach. The results match well with geology map of the study area, fieldwork data and laboratory analysis. Accuracy assessment of the mapping result represents a reasonable kappa coefficient (0.70%) and appropriate overall accuracy (74.64%), which verified the robustness of the BRMT approach. This approach has great potential and capability for mapping sedimentary succession with diverse local–geological–physical characteristics around the world. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

AUTHOR KEYWORDS: ASTER; Band ratios matrix transformation (BRMT); Deh-Molla; Lithostratigraphy mapping; Sedimentary rocks
INDEX KEYWORDS: Infrared devices; Infrared radiation; Kaolinite; Linear transformations; Lithology; Matrix algebra; Mica; Photomapping; Principal component analysis; Remote sensing; Sedimentology; Silicate minerals, ASTER; Deh-Molla; Lithostratigraphy; Matrix transformation; Physical characteristics; Remote sensing imagery; Satellite remote sensing data; Visible near-infrared, Sedimentary rocks
PUBLISHER: MDPI AG

Askari, G., Li, Y., MoezziNasab, R. An adaptive polygonal centroidal Voronoi tessellation algorithm for segmentation of noisy SAR images (2014) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40 (2W3), pp. 65-68.

DOI: 10.5194/isprsarchives-XL-2-W3-65-2014

In this research, a fast, adaptive and user friendly segmentation methodology is developed for highly speckled SAR images. The developed region based centroidal Voronoi tessellation (R-BCVT) algorithm is a kind of polygon-based clustering approach in which the algorithm attempts to (1) split the image domain into j numbers of centroidal Voronoi polygons (2) assign each polygon a label randomly, then (3) classify the image into k cluster iteratively to satisfy optimum segmentation, and finally a k-mean clustering method refine the detected boundaries of homogeneous regions. The advantages of the novel method arise from adaptively, simplicity and rapidity as well as low sensitivity of the model to speckle noise.

AUTHOR KEYWORDS: Centroidal Voronoi tessellation; Clustering; Gamma distribution; SAR; Segmentation
INDEX KEYWORDS: Algorithms; Computational geometry; Geometry; Image segmentation; Iterative methods; Synthetic aperture radar, Based clustering; Centroidal Voronoi Tessellation; Clustering; Developed regions; Gamma distribution; Homogeneous regions; K-mean clustering methods; SAR, Clustering algorithms
PUBLISHER: International Society for Photogrammetry and Remote Sensing