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Goel S.,University of Delhi | Sharma A.,University of Delhi | Panchal V.K.,Defence Terrain Research Laboratory
Communications in Computer and Information Science

Remote sensing is the most relevant science that permits us to acquire information about the surface of the land, without having actual contact with the area being observed. Amongst the multiple uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Multi spectral classification of remotely sensed data has been widely used to generate thematic Land-Use/Land-Cover maps. Two of the extensively used algorithms for image classification are Self Organizing Feature Maps (SOFM) and Ant Colony Optimization. Although both are bio-inspired optimization techniques, however combining them is a challenging task, especially in the field of remote sensing. In this paper, we have used a Self Organizing Ant Algorithm for Classification of remotely sensed data. Also, we have suggested a new reinforcement factor for the pheromone updation. A test of algorithm is conducted by classifying a high resolution, multi-spectral satellite image of Alwar Region. Results obtained are encouraging. © 2011 Springer-Verlag Berlin Heidelberg. Source

Kumar A.,The LNM Institute of Information Technology | Panchal V.K.,Defence Terrain Research Laboratory
Proceedings of ICCCS 2014 - IEEE International Conference on Computer Communication and Systems

Discretization of continuous features is both a requirement and a way of performance enhancement for many machine learning algorithms. In this paper, we review previous work on continuous feature discretization, apply different discretization algorithms for image classification of a satellite remote sensing image and conduct an empirical evaluation of several methods. © 2014 IEEE. Source

Jaimini U.,Lakshmi Niwas Mittal Institute of Information Technology | Panchal V.K.,Defence Terrain Research Laboratory
Proceedings - 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013

Swarm Intelligence has emerged as an important technique among various computational techniques due to its effieciency and robustness of the solution. The authors have categorized the different swarm intelligence techniques based on the agents population involved and on space-time variation to get an optimal solution to a problem. © 2013 IEEE. Source

Arora G.,Banasthali University | Rana S.,Manipal University India | Panchal V.K.,Defence Terrain Research Laboratory
ICROIT 2014 - Proceedings of the 2014 International Conference on Reliability, Optimization and Information Technology

The main motive of Ant Colony Optimization (ACO) is to find the shortest path from the ants' nest to the food source by sensing the amount of pheromone (a chemical secreted by ants which is used as the communication system) on the different paths available. In this paper, we propose a novel methodology which solves the problem of an ant facing an obstacle in its path from the nest to the food source, in which case the conventional ACO may fail. This work proposes a modified ant colony optimization by introducing the concept of perception radius, for enabling the ants to find the path to the food source even if there is any break in the pheromone trail. The Travelling Salesman Problem, as an example, is solved using the proposed modified ACO and the results obtained are compared with Dijkstra's algorithm. It is clearly demonstrated that our methodology not only works well when breaks or hurdles are encountered by ants but also provides efficient results. © 2014 IEEE. Source

Gupta N.,Bhagwan Parshuram Institute of Technology | Panchal V.K.,Defence Terrain Research Laboratory
International Geoscience and Remote Sensing Symposium (IGARSS)

Mixed pixels are usually the biggest reason for lowered success in classification accuracy. Aiming at the characteristics of remote sensing image classification, the mixed pixel problem is one of the main factors that affect the improvement of classification precision in image. How to decompose the mixed pixels precisely and effectively for multispectral/hyper spectral remote sensing images is a critical issue for the quantitative research. As Remote sensing data is widely used for the classification of types of land cover such as vegetation, water body thus Conflicts are one of the most characteristic attributes in satellite multilayer imagery. Conflict occurs in tagging class label to mixed pixels that encompass spectral response of different land cover on the ground element. In this paper we attempted to present a new approach for resolving the mixed pixels using Biogeography based optimization. The paper deals with the idea of tagging the mixed pixel to a particular class by finding the best suitable class for it using the concept of immigration and emigration. © 2011 IEEE. Source

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