SAR Image Coastline Detection Based on Regional Distance Regularized Geometric Active Contour Models

doi:10.11947/j.AGCS.2016.20160039

Abstract

Abstract: Synthetic aperture radar (SAR) satellite remote sensing images can greatly increase the frequency of the coastline coverage all over the country. However, due to the influence of the random sea surface roughness caused by waves, it's a challenge to distinguish the coastline and sea boundary. To solve this problem, this paper proposes regional distance regularized geometric active contour models (RDRGAC), in which the distance regularized term is introduced to avoid periodically initializing the degraded function with a signed distance function, accelerating the speed of convergence. Besides, it establishes the nonlinear regression relationship between the regional term parameters and equivalent number of looks (ENL) in SAR images, leading to the adaptive setting of RDRGAC model with different SAR images, which could improve the accuracy of coastline automatic detection in return. In the experiments, SAR images in Beidaihe and Dalian Jinzhou Bay respectively are adopted to detect the coastline, verifying the effective of the proposed method.

Key words: speckle noise, synthetic aperture radar, nonlinear regression relationship, geometric active contour model, equivalent number of looks

CLC Number:

P237

JIANG Dawei, FAN Jianchao, HUANG Fengrong. SAR Image Coastline Detection Based on Regional Distance Regularized Geometric Active Contour Models[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9): 1096-1103.

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