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- W3152990493 abstract "Image classification is the technique of extracting land use land cover information from the remotely sensed data to create thematic maps. In this paper, we subject the coastal agroclimatic zone data of the Udupi district boundary, India, to three unsupervised classification techniques: chaincluster, k-means, and iterative self-organizing data analysis technique (ISODATA). For this purpose, we have employed Sentinel-2A multispectral remote sensor data. This study aims to assess the classification performance of the above said classifiers in their ability to identify level-1 and level-2 classes of Anderson’s classification system. The study also aims to identify the spatially and spectrally dominant classes in the study area. The classification outcomes are duly subjected to an accuracy assessment procedure to calculate the overall classification accuracy as well as individual class accuracy. We notice from the results obtained that the ISODATA method yields substantially improved classification results compared to chaincluster and k-means techniques." @default.
- W3152990493 created "2021-04-26" @default.
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- W3152990493 date "2021-01-01" @default.
- W3152990493 modified "2023-10-17" @default.
- W3152990493 title "Land Cover Mapping Capability of Chaincluster, K-Means, and ISODATA techniques—A Case Study" @default.
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- W3152990493 doi "https://doi.org/10.1007/978-981-16-0443-0_23" @default.
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