Rivers and Coastlines Detection in Multispectral Satellite Images Using Level Set Method and Modified Chan Vese Algorithm

Abstract

The paper discusses wide variety of ways in which multispectral satellite images are being utilized in coastline and river detection. Flooding is a major problem in which causes distraction to the natural resources. River detection in satellite images is useful in flood monitoring, tracing sedimentation along the river bank and tracking dry outs of the major rivers. Coastline detection is an important for coastline zone monitoring, extraction and analysis of coastline changes which are caused by gradual washing out of sand or by abrupt natural calamity. The proposed work presents an approach for detecting rivers and coastlines over water bodies by the Level Set (LS) Approach and Chan Vese (CV) algorithm. CV approach was initially designed for the medical imaging. In the proposed work CV method is modified with respect to the contour smoothening parameters and time step which further improves the algorithm accuracy for the river and coastline detection. Based on the experimental results we compared LS segmentation method with the modified CV model both subjectively and objectively. For objective analysis measures like Dice coefficient, computation time and Hausdorff Distance are used.