Preserving Texture Boundaries for SAR Sea Ice Segmentation

• Main Author: Jobanputra, Rishi
• Format: Master Thesis
• Language: English
• Published: University of Waterloo 2004
• Subjects: Systems Design; Grey level co-occurrence probabilities; texture analysis; image segmentation; SAR sea ice; texture features; Sea ice
• Online Access: http://hdl.handle.net/10012/913

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability ( GLCP ) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the GLCP method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as WGLCP (weighted GLCP ) texture features. In this research, the WGLCP and GLCP feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The WGLCP method outperforms the GLCP method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the GLCP correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the GLCP correlation statistical feature decreases segmentation accuracy. When comparing WGLCP and GLCP features for segmentation, the WGLCP features provide higher segmentation accuracy.