Support vector machine as a binary classifier for automated object detection in remotely sensed data

8th International Symposium of the Digital Earth

地球科学;计算机科学

Wardaya, P.D.^1

Geosciences and Petroleum Engineering Faculty, Universiti Teknologi PETRONAS, Tronoh, Perak Darul Ridzuan 31750, Malaysia^1

关键词: Accurate analysis;    Binary classifiers;    Limited training data;    Remote sensing images;    Remotely sensed data;    SVM algorithm;    Synthetic images;    Training data;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012014/pdf
DOI  :  10.1088/1755-1315/18/1/012014

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.