Support vector machine for seagrass and benthic bottom types classification using high resolution coastal digital aerial images

Issue Date

2016

Abstract

The need for mapping Submerged Aquatic Vegetation (SAV) is essential because the benthic habitats are important component of the Philippine marine ecosystem and food chain. A classification system was evaluated for mapping and monitoring benthic habitats and bottom types in shallow tropical marine waters using digital aerial images. The study shows that the use of a consumer-based digital camera mounted on an unmanned aerial vehicle can generate an orthophoto for remote sensing application. The subsequent use of support vector machine is a good classification tool which provided a test result of 85% accuracy for seagrass, and an overall accuracy of above 80% for SAV and bottom type mapping. This accuracy sufficiently provides basis for the local government units in administering rules and regulations in preserving and managing their coastlines.

Source or Periodical Title

Far East Journal of Electronics and Communications

ISSN

0973-7006

Volume

Special volume 2

Page

69-76

Document Type

Article

Physical Description

illustrations

Language

English

Subject

Coastal mapping, Digital image processing, SAV, SVM, Unmanned aerial vehicle

Identifier

DOI:10.17654/ECSV216069.

Digital Copy

yes

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