Improvement of Hydrological Network Model Using Object-based Classification based from InfoGain Feature Selection

被引：0

作者：

Bentir, Sarah Alma P. ^{[1
]}

Ballado, Alejandro H., Jr. ^{[1
]}

Balan, Ariel Kelly D. ^{[1
]}

Lazaro, Jose B. ^{[1
]}

机构：

[1] Mapua Univ, Sch Informat & Technol, Sch Elect Elect & Comp Engn, Manila 1002, Philippines

来源：

2017 IEEE 9TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT AND MANAGEMENT (IEEE HNICEM) | 2017年

关键词：

InfoGain; Feature selection; GEOBIA; Weighted Flow Accumulation; Support Vector Machine; Stratified Random Sampling; LANDSCAPES;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

this study aims to cover the gap between the geometry and topology in drainage network classification by creating a model produced using a weighted flow function. The proposed methodology focused on building a model from Object-Based Image Analysis (OBIA) based from the sophisticated parameter to be used as the weight input to morphological analysis. This study performed segmentation evaluation using Area Fit Index. Further, to improve classification performance, this study performed feature selection using InfoGain based from the stratified random sampling in python.

引用

页数：6

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