Title :
Comprehensive performance analysis of Spatio-Temporal Data Mining approach on multi-temporal coastal remote sensing datasets
Author :
Gokaraju, Balakrishna ; Durbha, Surya S. ; King, Roger L. ; Younan, Nicolas H.
Author_Institution :
Center for Adv. Vehicular Syst. (CAVS), Mississippi State Univ., Starkville, MS, USA
Abstract :
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´).
Keywords :
data mining; feature extraction; geophysics computing; learning (artificial intelligence); oceanographic techniques; remote sensing; support vector machines; GLCM textural feature; HAB detection; SeaWiFS sensor data; comprehensive performance analysis; machine learning based spatiotemporal data mining approach; multitemporal coastal remote sensing datasets; support vector machines; textural feature extraction; wavelet textural feature; Analytical models; Data mining; Data models; Feature extraction; Remote sensing; Sea measurements; Support vector machines; Machine Learning; Spatio-Temporal; Support Vector Machines;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4577-1003-2
DOI :
10.1109/IGARSS.2011.6049592
