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Comprehensive performance analysis of Spatio-Temporal Data Mining approach on multi-temporal coastal remote sensing datasets

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4 Author(s)
Gokaraju, B. ; Center for Adv. Vehicular Syst. (CAVS), Mississippi State Univ., Starkville, MS, USA ; Durbha, S.S. ; King, R.L. ; Younan, N.H.

The present study discusses about the new textural feature extraction, its improvement and a comprehensive analysis of our previous Machine Learning based Spatio-Temporal (STML-HAB) Data Mining approach for HAB detection mentioned in Ref. [2]. This study is an elaborative analysis extending our first results presented in Ref. [2]. The additional Wavelet and GLCM textural features helped in improving the performance up to an accuracy of 0.9259 'K' using SeaWiFS sensor data. This is a significant improvement of almost 17% compared to our first results with an accuracy of (0.7513 'K').

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Date of Conference:

24-29 July 2011

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