Classification of Satellite broadcasting Image and Validation Exhausting Geometric Interpretation

Classification of Land Use/Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification by visual interpretation technique is accurate but time consuming and requires field experts. To overcome these difficulties, the present research work investigated efficient and effective automation of satellite image classification. Automated classification approaches are broadly classified in to i) Supervised Classification ii) Unsupervised Classification iii) Object Based Classification. This paper presents classification capabilities of K-Means, Parallel Pipe and Maximum Likelihood classifiers to classify multispectral spatial data (LISS-4). Using statistical inference, classified results are validated with reference data collected from field experts. Among three, Maximum Likelihood classifier (MLC) gained a significant credit in terms of getting maximum Overall accuracy and Kappa Factor.