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').

引用

页码：2153 / 2156

页数：4

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