Detection and Monitoring of Geomorphic Landforms in Areas with Shadow and Cloud Cover Using Remote Sensing Techniques and Fuzzy Segmentation


1 Assistant Professor Department of Marine Geology, Faculty of Marine Natural Resources, Khorramshahr University Marine Science and Technology, Khorramshahr, Iran.

2 Chief Innovation Office, Sinenta Corp, Almeria, Spain


In this study, with the aim of detecting and monitoring geological lines (ridges and thalweg) as well as correcting and reducing atmospheric effects (shadow and cloud cover) from HR-PRS panchromatic images of GeoEye-1 sensor from the northern highlands in the Middle Zagros have been used. In this regard, after radiometric and geometric processing, based on fuzzy properties, the input images are integrated in MATLAB software and then using MSA, FWS, IDF and CFM algorithms, fuzzy segmentation of HR-PRS panchromatic images was performed. In general, the results of applying the studied fuzzy segmentation algorithms on the study area show that the Interval-valued Data Fuzzy c-means (IDF) method has the best performance in detecting lineaments. But the difference in the performance of this algorithm is in the fuzzy segmentation of the lineaments in the cloud shadow, which is of a radiometric nature, and this has been done correctly. This is due to the use of fuzzy numbers, noise resistance and remote data, as well as textural, structural and spectral properties for efficient clustering and target identification in this method. The results of this study prove the effectiveness and efficiency of the fuzzy segmentation algorithms studied in detecting lines and eliminating cloud cover and shadows in HR-PRS satellite images. At the same time, these findings offer new ideas for remote sensing studies, especially in the field of precise information extraction from images and image processing.


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