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Sadalage, Jyoti; Arnab, Cdr. Dr. D; Pawar, Nalinee (2015)
Publisher: Journal of Engineering Computers & Applied Sciences
Journal: Journal of Engineering Computers & Applied Sciences
Languages: English
Types: Article
Subjects: Engineering; Department of Electronics & Telecommunications, Side scan sonar, Sediment classification, Wavelet transform
The nature of seafloor/river floor affects on areas such as defense, environmental (habitat mapping and protection), economic (fisheries, mining), and maritime (dredging of harbors and channels). To predict the capacity of the river dam to store the water, it is essential to know the topography of the river floor. Collection of samples of seafloor/river floor sediment and their characterizations are tedious and time consuming tasks even for small areas. Thus acoustic remote sensing techniques are useful for rapid seafloor/river floor characterization. In this paper texture images from side scan sonar are used for sediment classification. Textures are analyzed by using Haar wavelet transform. Local statistical features are extracted from first level and second level decomposition. The Side Scan SONAR images used in this study are from EdgeTech DF1000 side scan sonar data, a part of project REBENT, IFREMER
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