Sea-ice discrimination using texture analysis with feature selection over Sentinel-۱ images
Publish place: 1st International Biennial Conference of Artificial Intelligence and Data Science 2024
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
DSAI01_015
تاریخ نمایه سازی: 4 تیر 1403
Abstract:
This paper investigates sea-ice discrimination using SAR images through the utilizationof GLCM (Gray-Level Co-occurrence Matrix) feature extraction coupled with L-scorefeature selection. By focusing on the specific challenge of distinguishing between sea and ice,we aim to streamline the process while maintaining accuracy. Our approach efficiently extractstexture features from Sentinel-۱ images and employs L-score feature selection to mitigate computationalburden without compromising discrimination efficacy. This methodology offers apromising avenue for expediting sea-ice discrimination tasks, essential for various remote sensingand environmental monitoring applications. At the end, this offers significant time savings byapplying the feature selection method, which can happen with almost the same accuracy.
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Authors
Parsa Shamsaddini
Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Busher, Iran
Ahmad Keshavarz
Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Busher, Iran
Hojat Ghimatgar
Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Busher, Iran
Stefano Zecchetto
Istituto di Scienze Polari, Consiglio Nazionale delle Ricerche ,Padova, Italy