Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Soh, Leen-Kiat, Tsatsoulis, Costas
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2007
• Subjects: Co-occurrence matrix; Sar; Sea ice; Texture; http://id.worldcat.org/fast/1110029
• Online Access: http://hdl.handle.net/1808/1287; https://doi.org/10.1109/36.752194

©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture, We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures, We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured, These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.