An effective hybrid approach to remote-sensing image classification |
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| Authors: | Aravind Harikumar Anil Kumar Alfred Stein P.L.N. Raju Y.V.N. Krishna Murthy |
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| Affiliation: | 1. Department of Geoinformatics, Indian Institute of Remote Sensing (IIRS), Dehradun, Uttarakhand 248001, Indiaaravind.harikumar@unitn.it;3. Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing (IIRS), Dehradun, Uttarakhand 248001, India;4. Department of Geoinformatics, ITC – Faculty of Geo-Information Science and Earth Observation, Enschede, The Netherlands;5. Remote Sensing &6. Geoinformatics Group, Indian Institute of Remote Sensing (IIRS), Dehradun, Uttarakhand 248001, India;7. Indian Institute of Remote Sensing (IIRS), Dehradun, Uttarakhand 248001, India |
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| Abstract: | This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of the second-order pixel neighbourhood was modelled using Markov random fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothing at the class boundaries. The hybrid classifier was applied to advanced wide-field sensor (AWiFS) and linear imaging self-scanning sensor-III (LISS-III) images from a rural area in India. Validation was done with a LISS-IV image from the same area. The highest increase in accuracy among the adaptive functions was 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images. |
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