Object Based Classification Approach for impervious and non-impervious surfaces in satellite images Sujuma Basumatary (1), R.D. Garg (2), Pankaj Pratap Singh (3*)
1) Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, India
2) Geomatics Engineering, Department of Civil Engineering, Indian Institute of Technology Roorkee, India
3) Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, India
*pankajp.singh[at]cit.ac.in
Abstract
In the last few years, satellite image classification is gaining more attention due to the availability of remotely sensed imagery^s in high spatial resolution. This paper approached a non-linear type object classification approach based on object basis which incorporates K-Nearest Neighbour (KNN) algorithm for segmentation and classification. This proposed approach is based on object based image analysis (OBIA) technique. Spatial information is playing an important role in this technique. In this work, various features are extracted and utilized for the classification of non-linear objects. Spectral features of the training image objects are extracted using region of image (ROI) based samples which are used in KNN algorithm for segmentation and classification with a good level of accuracy. Images are classified in five types of objects such as road, building, land, water body, and vegetation also. In addition, parking lots are also having sometimes similar types of spectral reflectance as road due to similar material in both. The primarily focus of this work is to extract the non-linear objects by avoiding misclassification in a compact manner and also to improve the visibility of object.
