http://dspace.dtu.ac.in:8080/jspui/handle/123456789/49 2026-04-28T04:03:56Z 2026-04-28T04:03:56Z GUPTA, DAYA GOEL, LAVIKA PANCHAL, V.K. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13998 2014-12-19T11:01:19Z 2012-01-01T00:00:00Z

Title: BIOGEOGRAPHY AND PLATE TECTONICS BASED OPTIMIZATION FOR WATER BODY EXTRACTION IN SATELLITE IMAGES Authors: GUPTA, DAYA; GOEL, LAVIKA; PANCHAL, V.K. Abstract: Recent advances in remote sensing have widened the platform for research in science and technology. Undoubtedly the estimation of geo-bio-physical properties of the land cover features like water, urban, vegetation, rocky and barren areas play an important role in environmental, transportation and region planning, natural disaster, industrial and agricultural production. Since the water transport is cheapest, extraction of the water body in hyper spectral images of remote areas for which we don’t have enough details of its terrain is inevitable. Till now, natural computation and bioinspired intelligent techniques like DNA computing, membrane computing, genetic algorithms, neural computing have been used for demonstrating the applications of computational intelligence in the field of remote sensing. However, geoscience has never been used as a nature inspired intelligent technique for developing a computational model. This paper demonstrates the evolution of a new geo-science based approach for satellite image processing using an analogy between plate tectonics and biogeography based optimization. The paper presents biogeography and plate tectonics based optimization (BPBO) as a powerful paradigm for identifying water body area in the satellite image and hence, make a significant contribution towards the development of a new computational intelligence technique in the field of AI. Our major assumption is that it is the entropy which is the driving force leading to the formation of heterogeneous regions called as plates, similar to the convection force in the mantle of the Earth.

2012-01-01T00:00:00Z GOEL, LAVIKA http://dspace.dtu.ac.in:8080/jspui/handle/repository/13996 2014-12-19T11:01:25Z 2010-12-01T00:00:00Z

Title: LAND COVER FEATURE EXTRACTION USING HYBIRD SWARM INTELLIGENCE TECHNIQUE – A REMOTE SENSING PERSPECTIVE Authors: GOEL, LAVIKA Abstract: The findings of recent studies are showing strong evidence to the fact that some aspects of biogeography can be applied to solve specific problems in science and engineering. The proposed work presents a hybrid biologically inspired technique that can be adapted according to the database of expert knowledge for a more focused satellite image classification. The paper also presents a comparative study of our hybrid intelligent classifier with the other recent Soft Computing Classifiers such as ACO, Hybrid Particle Swarm Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough- Fuzzy Tie up and the Semantic Web Based Classifiers and the traditional probabilistic classifiers such as the Minimum Distance to Mean Classifier (MDMC) and the Maximum Likelihood Classifier (MLC).

2010-12-01T00:00:00Z GUPTA, DAYA GOEL, LAVIKA PANCHAL, V.K. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13995 2014-12-19T11:20:29Z 2011-05-01T00:00:00Z

Title: EMBEDDING EXPERT KNOWLEDGE TO HYBRID BIO-INSPIRED TECHNIQUES – AN ADAPTIVE STRATEGY YOWARDS FOCUSSED LAND COVER FEATURE EXTRACTION Authors: GUPTA, DAYA; GOEL, LAVIKA; PANCHAL, V.K. Abstract: The findings of recent studies are showing strong evidence to the fact that some aspects of biogeography can be adaptively applied to solve specific problems in science and engineering. This paper presents a hybrid biologically inspired technique called the ACO2/PSO/BBO (Ant Colony Optimization2/ Particle Swarm Optimization / Biogeography Based Optimization) Technique that can be adapted according to the database of expert knowledge for a more focussed satellite image classification. The hybrid classifier explores the adaptive nature of Biogeography Based Optimization technique and therefore is flexible enough to classify a particular land cover feature more efficiently than others based on the 7-band image data and hence can be adapted according to the application. The paper also presents a comparative study of the proposed classifier and the other recent soft computing classifiers such as ACO, Hybrid Particle Swarm Optimization – cAntMiner (PSO-ACO2), Hybrid ACO-BBO Classifier, Fuzzy sets, Rough-Fuzzy Tie up and the Semantic Web Based classifiers with the traditional probabilistic classifiers such as the Minimum Distance to Mean Classifier (MDMC) and the Maximum Likelihood Classifier (MLC). The proposed algorithm has been applied to the 7- band cartoset satellite image of size 472 X 576 of the Alwar area in Rajasthan since it contains a variety of land cover features. The algorithm has been verified on water pixels on which it shows the maximum achievable efficiency i.e. 100%. The accuracy of the results have been checked by obtaining the error matrix and KHAT statistics .The results show that highly accurate land cover features can be extracted effectively when the proposed algorithm is applied to the 7-Band Image , with an overall Kappa coefficient of 0.982.

2011-05-01T00:00:00Z GOEL, LAVIKA GUPTA, DAYA PANCHAL, V.K. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13993 2014-12-19T10:58:29Z 2011-10-01T00:00:00Z

Title: INFORMATION SHARING IN SWARM INTELLIGENCE TECHNIQUES: A PERSPECTIVE APPLICATION FOR NATURAL TERRAIN FEATURE ELICITATION Authors: GOEL, LAVIKA; GUPTA, DAYA; PANCHAL, V.K. Abstract: Swarm intelligence (SI) is an Artificial Intelligence technique based on the study of collective behaviour in decentralized, self-organizing systems. It enables relatively simple agents to collectively perform complex tasks, which could not be performed by individual agents separately. Particles can interact either directly or indirectly (through the environment). The key to maintain global, self-organized behaviour is social interaction i.e. information sharing between the system's individuals. Hence, information sharing is essential in swarm intelligence. In this paper, we highlight how the concept of information sharing in various swarm-based approaches can be utilised as a perspective application towards the elicitation of natural terrain features. The paper provides a mathematical formulation of the concept of information sharing in each of the swarm intelligence techniques of Biogeography based optimization (BBO), Ant Colony Optimization (ACO), Particle Swarm optimization (PSO) and Bee Colony Optimization (BCO) which are the major constituents of the SI techniques that have been used till date for classifying topographical facets over natural terrain.

2011-10-01T00:00:00Z