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This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been found to be the most suitable for noisy ocean colour images. In the attempted technique, partial removal of the noise in the images is performed and the edges are detected using entropic method. In our approach, Jhensen-Shannon divergence for the images is calculated, and the divergence image is arrived at after applying an appropriate threshold and filter to estimate the gradients. An attempted case study on retrieving chlorophyll front edges using this technique indicates that entropic method is far superior to conventional edge-enhancement tools, in terms of its insensitivity to impulsive noises and, capability in detecting meso- and micro-scale changes. This procedure would largely decrease the ambiguities associated with the ocean colour edges and hence has promising application potential in targeting fishing zones, sediment dispersion modeling and climate related studies.

advances in remote sensing

Detection of Fronts from Ocean Colour Monitor Images Using Entropic Technique: A Case Study of Meso- and Micro-Scale Chlorophyll Mapping in Bay of Bengal, India