Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution |
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| Authors: | A Kumar V K Dadhwal |
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| Institution: | 1.Indian Institute of Remote Sensing,Dehradun,India |
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| Abstract: | In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed
pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy
c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets
from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification
and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy
based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM).
It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based
classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty. |
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