Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Prasad, R.Subject: search.filters.subject.Genetic algorithm

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    A new SIFT matching criteria in a genetic algorithm framework for registering multisensory satellite imagery
    (Association for Computing Machinery acmhelp@acm.org, 2014) Senthilnath, J.; Prasad, R.
    Synthetic Aperture Radar (SAR) images are efficient and reliable source of information in extraction of damaged regions in case of floods. In assessment of damage accurately due to floods, image registration of optical (before-flood) and SAR images (after-flood) has to be carried out efficiently. In this paper, we discuss a robust multi-sensor image registration algorithm using scale invariant feature points for keypoint extraction. For matching the keypoints, a multi-objective genetic algorithm is developed with angle, distance and vicinity criterion as the fitness functions. This optimization process helps in matching the scale invariant feature points. From the obtained results, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2014 ACM.
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    Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2015) Senthilnath, J.; Omkar, S.N.; Mani, V.; Prasad, R.; Rajendra, R.; Shreyas, P.B.
    This paper investigates a new approach for flood evaluation based on multi-sensor satellite images utilizing swarm intelligence techniques. The swarm intelligence techniques used are Genetic Algorithm (GA) for image registration and Niche Particle Swarm Optimization (NPSO) for image clustering. Analysis of satellite images are applied in two stages: Linear Imaging Self Scanning Sensor (LISS-III) image acquired before-flood and Synthetic Aperture Radar (SAR) image acquired during-flood. In the first step, SAR image is aligned with LISS-III image using GA. The aligned SAR image (during-flood) is used to extract flooded and non-flooded regions where as LISS-III image (before-flood) is used to classify various land cover regions. For this image clustering is carried out where cluster centers are generated using the cluster splitting technique such as NPSO. The data points are grouped into their respective classes using the merging method. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. The performance comparisons of these swarm intelligence techniques with conventional methods are presented. © 2015 IEEE.