A Hybrid Level Set Based Approach for Surface Water Delineation using Landsat-8 Multispectral Images

• Published in: Engineering letters Vol. 29; no. 2; p. 624
• Main Authors: Bijeesh, TV, Narasimhamurthy, KN
• Format: Journal Article
• Language: English
• Published: Hong Kong International Association of Engineers 17.05.2021
• Subjects: Algorithms; Change detection; Correlation coefficients; Delineation; Image classification; Image segmentation; Landsat satellites; Machine learning; Satellite imagery; Set theory; Similarity; Spectra; Surface water
• ISSN: 1816-093X; 1816-0948

The detection and delineation of surface water is a crucial step in change detection studies on water bodies using satellite images. Single band methods, spectral index methods, classification using machine learning and spectral unmixing methods are the widely used strategies for surface water mapping from multi-spectral images. Level set theory based algorithms have been successfully employed in image segmentation problems and are proven to be effective. This study presents a hybrid level set theory based segmentation algorithm which is a combination of edge based and region based approaches to detect and delineate surface water bodies in Landsat 8 images. Level set algorithms were applied in combination with Modified Normalized Difference Water Index (MNDWI) to further improve the delineation accuracy. Robustness of the proposed approach was established by successfully applying the algorithm to delineate water bodies of different sizes, ranging from 0.5 km2 to 298 km2 in surface area. The proposed algorithm was also compared with established machine learning based delineation methods and found to be faster than the algorithms those produced comparable delineation outputs. As the ground truth was not available for accuracy measurement, the output image of the proposed method was compared with the outputs of the machine learning algorithms using Pearsons correlation coefficient, Structural Similarity Index (SSIM) and Dice Similarity Index. The proposed algorithm was subsequently applied to multi-temporal Landsat data for water body change detection and analysis.