Geomorphology
heeva elmizadeh; Hadi Mahdipour
Abstract
The purpose of this research is the automatic recognition of morphic patterns of drainage network in the center of Qeshm Island using High Resolution Panchromatic Remotely Sensed (HR-PRS) and fuzzy clustering algorithms. It also investigates the efficiency of these methods in the GeoEye-1 satellite imagery ...
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The purpose of this research is the automatic recognition of morphic patterns of drainage network in the center of Qeshm Island using High Resolution Panchromatic Remotely Sensed (HR-PRS) and fuzzy clustering algorithms. It also investigates the efficiency of these methods in the GeoEye-1 satellite imagery segmentation of the study area in order to detect geomorphic features in areas with cloud and shadow coverage. In this regard, fuzzy segmentation of HR-PRS panchromatic images of the study area, after radiometric and geometric preprocessing using FWS, MSA, IDF and CFM algorithms, was performed in MATLAB software. Finally, the studied fuzzy clustering algorithms with fuzzy parameters are applied to the input HR-PRS images and the results are discussed. The results show that the Classical Fusion Method and FCM (CFM) clustering algorithm has the best performance in the field of fuzzy segmentation and detection of the studied indices. . As a result, the image borders are well defined. The reason for this is the use of fuzzy numbers as well as efficient clustering methods in this method. These results also show that remote sensing technology, by providing multi-time images, can be a very good basis for monitoring and detecting environmental changes, detecting effects and accurately extracting information from images. Also, the use of clustering algorithms and fuzzy features is a suitable and optimal method for integrating HR-PRS satellite image information from a geographical area with the aim of segmentation.
