Conference Papers

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

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    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.
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    Analysis of Land Use Land Cover Change Detection Using Remotely Sensed Data for Kali River Basin
    (Springer Science and Business Media Deutschland GmbH, 2024) Sreejith, K.S.; Kumar, G.P.; Dwarakish, G.S.
    For the last two centuries, the Earth's land cover has undergone fast change, and all indications indicate that this trend will continue. This shift is being driven by economic development and population expansion. For the management of natural resources and the observation of environmental changes, land use and land cover (LULC) change has become a key element. Natural landscapes have undergone significant change as a result of anthropogenic activity, particularly in areas where population increase and climate change have a significant impact. To effectively manage the environment, especially water management, it is essential to understand how trends in land use and land cover (LULC) change. This study used remote sensing and geographic information systems (GIS) to examine changes in LULC patterns during a 20-year period in the Kali River Basin. LULC changes were mapped using multitemporal Landsat series satellite images. Landsat-5 image of 2002 and Landsat-8 image of 2022 were obtained for the purpose of the study. Maximum likely hood algorithm was used to detect areas of change with supervised classification, performed in ERDAS Imagine 2014 and took minimum of 100 samples and maximum of 250 samples of ground truth data for each class. The supervised classification produced good results with overall accuracies of 91.58% and 89.47% for the 2002 and 2022, respectively. The results of the change detection analysis conducted between 2002 and 2022 demonstrate the extent of LULC changes that have taken place in various LULC classes, while the majority of the river basin's grassland, barren land, and open forest have undergone intensive conversion to cultivated land and built-up areas. These modifications show that population growth was responsible for the rise in cultivated land and built-up areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.