Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Multi-spectral satellite image classification using Glowworm Swarm Optimization(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Shenoy B, A.This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall. © 2011 IEEE.Item Multi-objective optimization of satellite image registration using Discrete Particle Swarm Optimisation(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karthikeyan, T.A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient. © 2011 IEEE.Item Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2012 IEEE.Item Quasi-based hierarchical clustering for land cover mapping using satellite images(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Raj, A.; Omkar, S.N.; Mani, V.; Kumar, D.This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC).The main objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach-NPSO with quasi distribution(Faure).These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification. © 2013 Springer.Item Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Vikram Shenoy, H.; Omkar, S.N.; Mani, V.This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region. © 2013 Springer.Item An optimal fuzzy logic controller tuned with artificial immune system(Springer Verlag service@springer.de, 2013) Omkar, S.N.; Ramaswamy, N.; Ananda, R.; Venkatesh, N.G.; Senthilnath, J.In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed FLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The FLC's were also tested in the presence of faulty rules. Finally, Kruskal-Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS. © 2013 Springer.Item Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment(2012) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient. © 2012 Springer-Verlag.Item Clustering using levy flight cuckoo search(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. © 2013 Springer.Item Location management in mobile computing using swarm intelligence techniques(Springer Verlag service@springer.de, 2014) Goel, N.; Senthilnath, J.; Omkar, S.N.; Mani, V.Location management is an important and complex issue in mobile computing. Location management problem can be solved by partitioning the network into location areas such that the total cost, i.e., sum of handoff (update) cost and paging cost is minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is NP-complete problem. In this paper, we present two swarm intelligence algorithms namely genetic algorithm (GA) and artificial bee colony (ABC) to obtain minimum cost in the location management problem. We compare the performance of the swarm intelligence algorithms and the results show that ABC give better optimal solution to locate the optimal solution. © Springer India 2014.Item 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.