Performance analysis of different classifier for remote sensing application
- Title
- Performance analysis of different classifier for remote sensing application
- Creator
- Mahendra H.N.; Mallikarjunaswamy S.; Rekha V.; Puspalatha V.; Sharmila N.
- Description
- The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of todays remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data. BEIESP.
- Source
- International Journal of Engineering and Advanced Technology, Vol-9, No. 1, pp. 7153-7158.
- Date
- 2019-01-01
- Publisher
- Blue Eyes Intelligence Engineering and Sciences Publication
- Subject
- Multispectral data; Pixel-based; Remote Sensing; Support Vector Machines
- Coverage
- Mahendra H.N., Department of ECE, JSS Academy of Technical Education, Visvesvaraya Technological University, Bangalore-560060 and Affiliated, Belagavi, Karnataka, India; Mallikarjunaswamy S., Department of ECE, JSS Academy of Technical Education, Visvesvaraya Technological University, Bangalore-560060 and Affiliated, Belagavi, Karnataka, India; Rekha V., Department of CSE, Christ University, Bangalore, India; Puspalatha V., Department of ISE, Mysore College of Engineering and Management, Mysore, India; Sharmila N., Department of EEE, RNSIT, Bangalore, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 22498958
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Mahendra H.N.; Mallikarjunaswamy S.; Rekha V.; Puspalatha V.; Sharmila N., “Performance analysis of different classifier for remote sensing application,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/16552.