@inproceedings{scholars5308,
           title = {Support vector machine as a binary classifier for automated object detection in remotely sensed data},
         address = {Kuching, Sarawak},
             doi = {10.1088/1755-1315/18/1/012014},
            note = {cited By 6; Conference of 8th International Symposium of the Digital Earth, ISDE 2014 ; Conference Date: 26 August 2013 Through 29 August 2013; Conference Code:108166},
          volume = {18},
          number = {1},
         journal = {IOP Conference Series: Earth and Environmental Science},
       publisher = {Institute of Physics Publishing},
            year = {2014},
            issn = {17551307},
          author = {Wardaya, P. D.},
        keywords = {Classification (of information); Image reconstruction; Object recognition; Oil spills; Satellite imagery, Accurate analysis; Binary classifiers; Limited training data; Object Detection; Remote sensing images; Remotely sensed data; SVM algorithm; Synthetic images, Support vector machines, accuracy assessment; algorithm; automation; data set; detection method; image analysis; image classification; satellite imagery},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902324450&doi=10.1088\%2f1755-1315\%2f18\%2f1\%2f012014&partnerID=40&md5=dda767668c60b0fc9c6f091da8427b83},
        abstract = {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. {\^A}{\copyright} Published under licence by IOP Publishing Ltd.}
}