Abstract
ABSTRACTScope of the present work is to apply modern methods of pattern recognition concerning the automatic detection of geomorphological features (curvilinear lineaments and topographic highs), with emphasis on geological faults and to compare the results of the automatic detection with aerial photographs and further geological data collected in Tinos Island. The contribution of this work in the geosciences is multidisciplinary because (a) it proposes to geoscientists a new tool of geomorphological analysis prior to the fieldwork and especially at the stage where published work and already available data are collected, evaluated and reprocessed, (b) it increases the detection accuracy of the geological features both prior and after the fieldwork, (c) it decreases the time of fieldwork and consequently the cost of the entire geological research and (d) it can be applied in digital elevation data. The automatically detected linear-curvilinear lineaments and topographic highs agree in location, shape and orientation with the ground truth data (geological maps, aerial photographs and field measurements). Furthermore, the shape and the orientation of the most prominent topographic high (Tsiknias) seems to be related to the tectonic regime in the wide area of study corresponding to the ENE-WSW, WSW-ESE, N-S and E-W trending normal faults.
Highlights
The increase of the remote sensed images and signals as well as the urgent need for the automatic information extraction and interpretation of such data sets have made the development and application of modern pattern recognition techniques a popular research topic for the last decades (Younan, Aksoy, & King, 2012)
A wide range of pattern recognition techniques have been proposed for both traditional application areas such as land cover and land use classification, road network extraction, and agricultural mapping and monitoring, as well as more recent topics such as monitoring of human settlements, management of natural resources, response planning for natural and human-induced disasters, assessment of the impact of climate change and conservation of biodiversity (Younan, Aksoy, & King, 2012)
The classification was conducted with the use of the pattern recognition methods on thin sections of five selected rocks
Summary
The increase of the remote sensed images and signals as well as the urgent need for the automatic information extraction and interpretation of such data sets have made the development and application of modern pattern recognition techniques a popular research topic for the last decades (Younan, Aksoy, & King, 2012). A wide range of pattern recognition techniques have been proposed for both traditional application areas such as land cover and land use classification, road network extraction, and agricultural mapping and monitoring, as well as more recent topics such as monitoring of human settlements, management of natural resources, response planning for natural and human-induced disasters, assessment of the impact of climate change and conservation of biodiversity (Younan, Aksoy, & King, 2012). Image processing and pattern recognition techniques have been used to map and monitor earthquakes, faulting, volcanic activity, landslides, flooding, wildfire and the damages associated with them (Joyce, Belliss, Samsonov, McNeill, & Glassey, 2009)
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