Preserving Texture Boundaries for SAR Sea Ice Segmentation

• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Sea ice
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.6469; http://www.eng.uwaterloo.ca/~dclausi/Theses/RishiJobanputraMASc2004.pdf

any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. 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