Assessment of multibeam backscatter texture analysis for seafloor sediment classification

• Main Authors: Samsudin, S. A., Hasan, R. C.
• Format: Article
• Language: English
• Published: International Society for Photogrammetry and Remote Sensing 2017
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/81364/1/SASamsudin2017_AssessmentofMultibeamBackscatterTextureAnalysis.pdf; http://eprints.utm.my/id/eprint/81364/; http://dx.doi.org/10.5194/isprs-archives-XLII-4-W5-177-2017
• Institution: Universiti Teknologi Malaysia

Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7km2, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.