Advances in Computational Research, ISSN: 0975–3273, Volume 2, Issue 1, 2010, pp-21-24 Copyright © 2010, Bioinfo Publications, Advances in Computational Research, ISSN: 0975–3273, Volume 2, Issue 1, 2010 Visualization techniques for data mining of Latur district satellite imagery Hiremath P.S. and Kodge B.G.* Department of Computer Science, Gulbarga University, Gulbarga, Karnataka State, India, hiremathps53@yahoo.com *Department of Computer Science, S. V. College, Udgir, Dist. Latur, Maharashtra, India, kodgebg@hotmail.com Abstract- This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery. The visualization tool is showcased in a classification study of high- resolution imageries of Latur district in Maharashtra state of India. Keywords: Data Mining, 3D space feature plot, Remote sensing, Visualization Introduction Image classification based on satellite imagery is a widely used technique for extracting thematic information on land cover This image processing step is the translation from spectral reflectance or digital numbers (DN) to thematic information. We classify objects by reducing a multiplicity of phenomena to a relatively small number of general classes (Tso and Mather, 2001). Classification is often performed to generalize a complex image into a relatively simple set of classes. A classified map is then used as input into a geographic information system (GIS) for further processing or analysis. Such inference is most often less than perfect and there is always an element of uncertainty in a classification result. As it can affect further processing steps and even decision making, it is important to understand, quantify and visualize the classification process. Visual Data Mining (VDM) is a powerful tool which is often overlooked in favour of traditional purely non-visual data mining, defined as the process of (semi-)automatically discovering meaningful patterns in data (Witten, 2005). VDM uses visual interaction to allow a human user to visually extract and explore patterns in data. When conducting a non-visual data mining, no matter how unbiased it may seem, the fact is that by simply choosing to carry out an automated analysis a priori assumptions have been made about what form the important results will take before analysis has actually begun (Simoff, 2002). By visually mining the data this prior bias can be removed. Whilst the bias is removed, subjectivity of the analysis is increased as it is based on a user’s perception, a point highlighted by many machine learning purists. However, this increased subjectivity is compensated for by a vastly increased degree of confidence in the analysis (Keim, 2002). VDM not only seeks to allow a human user to visually mine data but also to augment the non-visual data mining process. This augmentation usually takes the form of making the automated process more transparent to the user, hence providing increased confidence. VDM is not commonly applied in remote sensing applications. A traditional supervised remote sensing classification starts with a selection of training pixels or areas that represent specific land cover classes. The spectral and statistical properties of these pixels are then used to classify all unlabelled pixels in the image with a classification algorithm such as the widely used maximum likelihood classifier (commonly implemented in commercial remote sensing software). The accuracy of the classified map is tested with reference pixels that are not used in the training stage. Accuracy assessment usually takes the form of an error matrix with derived accuracy values such as the overall accuracy and the Kappa statistic. Although the error matrix provides an overall assessment of classification accuracy, it does not provide an indication of the spectral dissimilarity of class clusters, uncertainty related to the attribution of class labels to individual pixels, or the spatial distribution of classification uncertainty. In this study, we argue that VDM is an important tool for visual exploration of the data to improve insight into the classification algorithm and identify sources of spatial and thematic uncertainty. Recent studies showed that exploratory visualization tools can help to improve the image analyst’s understanding of uncertainty in a classified image scene. They proposed a combination of static, dynamic and interactive visualizations for exploration of classification uncertainty in the classification result. Lucieer (2004) and Lucieer and Kraak (2004) developed a visualization tool that allowed for visual interaction with the parameters of a fuzzy classification algorithm. The study showed that visualization of a fuzzy classification algorithm in a 3D feature space plot dynamically linked to a satellite image improves a user’s understanding of the sources and locations of uncertainty. In this study, we develop and present a new VDM prototype to visualize irregular shapes of class clusters and their spectral overlap in a 3D feature space plot. The tool helps to identify the location and shape of class clusters (showing spectral variance) and the overlap of these class clusters in 3D feature space to highlight sources of uncertainty in the training data for a spectral image classifier. To showcase the visualization prototype we present a classification study based on high-resolution IKONOS imagery of Latur district to assess the value of VDM in semi- automated image classification. This study is