Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol. 3, No.10, 2013 141 Image Classification in Remote Sensing Jwan Al-doski*, Shattri B. Mansor1 and Helmi Zulhaidi Mohd Shafri Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400, Serdang, Selangor, Malaysia * E-mail of the corresponding author: Jwan-83@hotmail.com Abstract One of the most important functions of remote sensing data is the production of Land Use and Land Cover maps and thus can be managed through a process called image classification. This paper looks into the following components related to the image classification process and procedures and image classification techniques and explains two common techniques K-means Classifier and Support Vector Machine (SVM). Keywords: Remote Sensing, Image Classification, K-means Classifier, Support Vector Machine 1. Image Classification Based on the idea that different feature types on the earth's surface have a different spectral reflectance and remittance properties, their recognition is carried out through the classification process. In a broad sense, image classification is defined as the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes (Lillesand, Keifer 1994). As can see in figure1. SPOT multispectral image of the test area Thematic map derived from the SPOT image using an unsupervised classification algorithm. Figure1. Example of Image Classification