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

fYear

2011

fDate

24-29 July 2011

Firstpage

2153

Lastpage

2156

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;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049592

Filename

6049592