Conference Papers

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

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Author: search.filters.author.Prasad, R.

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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.
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    Study of 2D localization using SIMO antenna
    (Institute of Electrical and Electronics Engineers Inc., 2016) Laddha, P.; Prasad, R.; Shripathi Acharya, U.S.; Srihari, P.; Prasad, S.; Rao, P.H.
    A single input and multiple output (SIMO) antenna configuration is presented for real time data acquisition. The proposed approach can provide both range and angle information in a plane. A method of localization of multiple point objects (targets) in azimuthal plane is realized by employing one stationary transmitting antenna and dynamic receiving antennas positions. FMCW chirp signals are used as transmitting signals. A parametric analysis is carried out by varying the bandwidth of transmitted signal, number of samples and number of antennas on a linear array with equal inter-element spacing. Radial resolution of 0.4 feet and 0.23 feet is achieved for 2GHz and 6 GHz bandwidths respectively. The method is extended to identify multiple targets. © 2016 IEEE.