Department of Computer Science and Information Systems

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    Applying Nature Inspired Metaheuristic Technique to Capture the Terrain Features
    (ICAI, 2012) Bharadwaj, Akanksha
    In recent years image classification has emerged as the most significant area of research in the field of remote sensing. Image classification helps us to acquire the geo-spatial information from the satellite data which can be useful to industries like defence, intelligence, natural resources etc. There exist various techniques like Biogeography Based Optimization (BBO), Ant Colony Optimization (ACO) etc for image classification. Here, we are applying a metaheuristic approach called Cuckoo Search in the area of image classification. The main advantage of this algorithm over other metaheuristic approach is that its search space is extensive in nature. The proposed methodology is applied to the Alwar region of Rajasthan. The image used is a 7 band image of 472 X 546 dimensions from Indian Remote Sensing Satellite Resiurcesat. This algorithm has captured almost all the terrain features and showed high degree of efficiency for almost all the regions (water, vegetation, urban, rocky, and barren) with a Kappa coefficient of 0.9465.
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    Remote Sensing Image Classification using Artificial Bee Colony Algorithm
    (Inder Science, 2014) Bharadwaj, Akanksha
    Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation.
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    Remote Sensing Image Classification using Artificial Bee Colony Algorithm
    (ICCSE, 2014-01) Bharadwaj, Akanksha
    Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation