Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/26132
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dc.contributor.authorAvashia, Vidhee-
dc.contributor.authorParihar, Shrutika-
dc.contributor.authorGarg, Amit-
dc.date.accessioned2023-03-21T10:06:14Z-
dc.date.available2023-03-21T10:06:14Z-
dc.date.issued2020-06-20-
dc.identifier.citationAvashia, V., Parihar, S., & Garg, A. X. (2020). Evaluation of Classification Techniques for Land Use Change Mapping of Indian Cities. Journal of the Indian Society of Remote Sensing, 48(6), 877–908. https://doi.org/10.1007/s12524-020-01122-7en_US
dc.identifier.issn0974-3006-
dc.identifier.urihttp://hdl.handle.net/11718/26132-
dc.description.abstractThis study looks into the development of multi-level classification approach for land use change mapping in Indian cities using Landsat imageries. In this study, we mapped 47 Indian cities at different time frames 1990, 2000, 2010, and 2017. We started with traditional classification methods, but results provided unsatisfactory accuracy levels. Thus, we employed multiple classification techniques to achieve results with higher accuracy. The paper captures the evaluation of different classification techniques—hybrid, unsupervised, decision tree classification (DTC), and object-based image analysis (OBIA). The results suggest improvement in accuracy levels by using multi-level classification for different cities at different stages of the classification process. The most prominent is the hybrid classification technique; 14 cities out of 47 reached to accuracy above 72% through hybrid classification. For problematic classes, we used DTC, OBIA, and unsupervised classification techniques after masking the datasets. DTC was used in cities with a greater number of problems in datasets. For example, in the case of Kochi City, the accuracy at the initial level was reported 51% through unsupervised classification which improved to 77% (supervised classification), and finally, it reached 90% by DTC technique. The overall accuracy achieved through the multi-level classification approach described in this paper for the 47 Indian cities ranges from 81 to 93%.en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofJournal of the Indian Society of Remote Sensing volumeen_US
dc.subjectMulti-level classificationen_US
dc.subjectCitiesen_US
dc.subjectAccuracyen_US
dc.subjectHybriden_US
dc.subjectUnsuperviseden_US
dc.subjectDTCen_US
dc.subjectOBIAen_US
dc.subjectIndiaen_US
dc.titleEvaluation of Classification Techniques for Land Use Change Mapping of Indian Citiesen_US
dc.typeArticleen_US
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