Soft classification for sub-pixel land cover extraction |
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| Authors: | R S Tiwari Manoj K Arora T Kailash |
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| Institution: | 1. Department of Civil Engineering, University of Roorkee, 247 667, Roorkee
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| Abstract: | With increasing resolution of the remotely sensed data the problems of images contaminated by mixed pixels arc frequent. Conventional
classification techniques often produce erroneous results when applied to images dominated by mixed pixels. This may load
to unrealistic representation of land cover, thereby, affecting efficient planning, management and monitoring of natural resources.
Consequently, soft classification techniques providing sub-pixel land cover information may have to be utilised.
From a range of soft classification techniques, the present study focuses on the utility of conventional maximum likelihood
classifier and linear mixture modelling for sub-pixel. land cover classifications. The accuracy of the soft classifications
has been assessed using distance measures and correlation co-efficient. The results show that linear mixture modelling has
produced accuracies comparable to maximum likelihood classifier. Besides this the correlations between actual land cover proportions
and proportions from linear mixture modelling, though not strong, arc statistically significant at 95% level of confidence.
It has also been observed that the normalised likelihoods of maximum likelihood classifier also show strong correlations with
the actual land cover proportions on ground and therefore has the potential to be used as a soft classification technique. |
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