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Please use this identifier to cite or link to this item: http://etd.iisc.ernet.in/2005/2280

Title: Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data
Authors: Uttam Kumar, *
Advisors: Ramachandra, T V
Mukhopadhyay, Chiranjit
Keywords: Remote Sensing - Data Processing - Algorithms
Image Fusion
Landscape Dynamics
Urban Growth - Modeling and Simulation
Pixel Classification
Geospatial Analysis - Algorithms
Multi-resolution Remote Sensing Data
Land Use Pattern Classification
Coarse Resolution Pixels
Spatial Metrics
Hybrid Bayesian Classifier
Cellular Automata
Submitted Date: Mar-2012
Series/Report no.: G25135
Abstract: Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of LC changes. This can be captured through the multi-resolution remote sensing (RS) data. However, with the advancements in sensor technologies, suitable algorithms and techniques are required for optimal integration of information from multi-resolution sensors which are cost effective while overcoming the possible data and methodological constraints. In this work, several per-pixel traditional and advanced classification techniques have been evaluated with the multi-resolution data along with the role of ancillary geographical data on the performance of classifiers. Techniques for linear and non-linear un-mixing, endmember variability and determination of spatial distribution of class components within a pixel have been applied and validated on multi-resolution data. Endmember estimation method is proposed and its performance is compared with manual, semi-automatic and fully automatic methods of endmember extraction. A novel technique - Hybrid Bayesian Classifier is developed for per pixel classification where the class prior probabilities are determined by un-mixing a low spatial-high spectral resolution multi-spectral data while posterior probabilities are determined from the training data obtained from ground, that are assigned to every pixel in a high spatial-low spectral resolution multi-spectral data in Bayesian classification. These techniques have been validated with multi-resolution data for various landscapes with varying altitudes. As a case study, spatial metrics and cellular automata based models applied for rapidly urbanising landscape with moderate altitude has been carried out.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/2935/G25135-Abs.pdf
URI: http://etd.iisc.ernet.in/handle/2005/2280
Appears in Collections:Management Studies (mgmt)

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