24 October 2016 Coastline change mapping using a spectral band method and Sobel edge operator
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Abstract
Coastline extraction has become an essential activity in wake of the natural disasters taking place in some regions such as tsunami, flooding etc. Salient feature of such catastrophes is lack of reaction time available for combating emergency, thus it is the endeavor of any country to develop constant monitoring mechanism of shorelines. This is a challenging task because of the magnitude of changes taking place to the coastline regularly. Previous research findings highlight a need of formulating automation driven methodology for timely and accurate detection of alterations in the coastline impacting sustainability of mankind operating in the coastal zone. In this study, we propose a new approach for automatic extraction of the coastline using remote sensing data. This approach is composed of three main stages. Firstly, classifying pixels of the image into two categories i.e. land and water body by applying two normalized difference indices i.e. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). Then, the process of binary conversion of classified image takes place using a local threshold method. Finally, the coastline is extracted by applying Sobel edge operator with a pair of (3×3) kernels. The approach is tested using 2.5m DubaiSat-1 (DS1) and DubaiSat-2 (DS2) images captured to detect and monitor the changes occurring along Dubai coastal zone within a period of six years from 2009 till 2015. Experimental results prove that the approach is capable of extracting the coastlines from DS1 and DS2 images with moderate human interaction. The results of the study show an increase of 6% in Dubai shoreline resulting on account of numerous man-made infrastructure development projects in tourism and allied sectors.
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Saeed Al-Mansoori, Saeed Al-Mansoori, Fatima Al-Marzouqi, Fatima Al-Marzouqi, } "Coastline change mapping using a spectral band method and Sobel edge operator", Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 100070P (24 October 2016); doi: 10.1117/12.2242046; https://doi.org/10.1117/12.2242046
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