sea-ice discrimination using texture analysis with feature selection over sentinel-1 images

This paper investigates sea-ice discrimination using sar images through the utilization of glcm (gray-level co-occurrence matrix) feature extraction coupled with l-score feature 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 extracts texture features from sentinel-1 images and employs l-score feature selection to mitigate computational burden without compromising discrimination efficacy. this methodology offers a promising avenue for expediting sea-ice discrimination tasks, essential for various remote sensing and environmental monitoring applications. at the end, this offers significant time savings by applying the feature selection method, which can happen with almost the same accuracy.