Version 1
: Received: 8 March 2019 / Approved: 11 March 2019 / Online: 11 March 2019 (09:01:44 CET)
How to cite:
Neware, R. Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data. Preprints2019, 2019030122. https://doi.org/10.20944/preprints201903.0122.v1
Neware, R. Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data. Preprints 2019, 2019030122. https://doi.org/10.20944/preprints201903.0122.v1
Neware, R. Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data. Preprints2019, 2019030122. https://doi.org/10.20944/preprints201903.0122.v1
APA Style
Neware, R. (2019). Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data. Preprints. https://doi.org/10.20944/preprints201903.0122.v1
Chicago/Turabian Style
Neware, R. 2019 "Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data" Preprints. https://doi.org/10.20944/preprints201903.0122.v1
Abstract
This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.
Keywords
Classification, SVM Classifier, ML Classifier, Supervised and Unsupervised Classification, Object-based Classification, Multispectral Data
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
