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The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multispectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. INTRODUCTIONIn recent times, satellite images are delivering an enormous source of information for studying the spatial and temporal variability of environmental conditions. It can be utilized in a number of applications which consists of making of mapping products for military and civil purposes, exploration, nursing of land use land cover [1], assessment of environmental damage, radiation level check, soil test, growth directive, crop outcome increment and urban planning. These multispectral images can be used mainly in the course of classification and also in mapping of vegetation over extensive spatial scales. This is because multispectral image classifies land cover and land usage features such as vegetation, water, oil, forests and urbanization, it also delivers very good scope and mapping. This kind of classification technique replaces the traditional classification techniques [2]. As many environmental and socio-economic proposals are based on these classification results, researches and studies on these satellite image classifications have centralized the concentration of the scientific community. A classification technique classifies relevant components or classes of land cover sections over a particular area [3]. Multi spectral images primarily consists the data that is collected over a wide range of frequencies and it changes for different locations [4]. This overall peculiar nature of satellite image data can be related to the spectral features that correlate with the spatial features belonging to the same band and this is called as spatial correlation. Classification of land use and land cover using remote sensing imagery is tough because of compound landscapes and also the spectral and spatial resolution of the satellite images.

Problem Statement: In many cases it would be helpful to include already existing data with the GIS for interpretation of remote sensing data. Object Based Image Analysis (OBIA) is a technology for image analysis which addresses exactly those problems hampering a successful bridging of Remote Sensing and GIS. In order to provide potential strategies to meet the proposed objective, a tentative SWOT Analysis is undertaken to identify current Strengths, Weakness, Opportunities and Threats that OBIA faces. Approach: Two different areas had been chosen for testing. The results of pixel based and polygon-based classification has been compared. Results: The classified image derived from polygon based classification is closer to human visual Interpretation. Finally, it is understood that using the e-cognition technique, the weaknesses and threats that OBIA originally suffered are minimized to a great extent. Conclusions: Pixel based classification uses the pure spectral information only. Beyond the pure spectral information, OBIA uses local context information also. This often essential information can be used together with form and texture features of image objects to improve classification significantly.

Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover since they provide data at regular intervals and is economical when compared to the other traditional methods of ground survey and aerial photography. Classification of multispectral remotely sensed data is investigated with a special focus on uncertainty analysis in the produced landcover maps. Here, we have proposed an efficient technique for classifying the multispectral satellite images using SVM into land cover and land use sectors. In the proposed classification technique initially pre-processing is done where the input image is subjected to a set of pre-processing steps which includes Gaussian filtering and RGB to Labcolorspace image conversion. Subsequently, segmentation using fuzzy incorporated hierarchical clustering technique is carried out. Then training of the SVM is carried out in the training data selection procedure and finally the classification step, where the cluster centroids are subjected to the trained SVM to obtain the land use and land cover sectors. The experimentation is carried out using the multi-spectral satellite images and the analysis ensures that the performance of the proposed technique is improved compared with traditional clustering algorithm