BENTHIC HABITAT CLASSIFICATION AND MAPPING USING SUPPORT VECTOR MACHINE ALGORITHM IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

摘要

This study demonstrates the application of classification techniques using Support Vector Machine (SVM) for benthic habitat mapping. The orthophotos of the coastal area of Hinatuan, Surigao Del Sur, which undergone quality checking, was used for this study. Optimization procedure was performed in matrix laboratory (MATLAB) software with parallel computing to help hasten the process due to its enormous size. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyperplanes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition software. The feature used for SVM algorithm are the following: CIE L~*a~*b~*, RGB Intensity, and One Dimensional Scalar Constancy. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analyses employed to orthophotos like the color transformation and illumination correlation and the use of SVM classification algorithm in mapping benthic habitats.