COMPREHENSIVE PERFORMANCE ANALYSIS OF SPATIO-TEMPORAL DATA MINING APPROACH ON MULTI-TEMPORAL COASTAL REMOTE SENSING DATASETS

被引：0
|
作者：
Gokaraju, Balakrishna [1 ]
Durbha, Surya S. [1 ]
King, Roger L. [1 ]
Younan, Nicolas H. [1 ]
机构：
[1] Mississippi State Univ, GeoResources Inst GRI, CAVS, Mississippi State, MS 39762 USA
来源：
2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年
关键词：
Spatio-Temporal; Machine Learning; Support Vector Machines;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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').
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页码：2153 / 2156
页数：4
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