relation: https://khub.utp.edu.my/scholars/5308/
title: Support vector machine as a binary classifier for automated object detection in remotely sensed data
creator: Wardaya, P.D.
description: In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result. © Published under licence by IOP Publishing Ltd.
publisher: Institute of Physics Publishing
date: 2014
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Wardaya, P.D.  (2014) Support vector machine as a binary classifier for automated object detection in remotely sensed data.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902324450&doi=10.1088%2f1755-1315%2f18%2f1%2f012014&partnerID=40&md5=dda767668c60b0fc9c6f091da8427b83
relation: 10.1088/1755-1315/18/1/012014
identifier: 10.1088/1755-1315/18/1/012014