A New Approach of Ensemble Learning Technique to Resolve the Uncertainties of Paddy Area through Image Classification

Remote Sensing

Shiuan Wan¹  Shih-Chieh Wu²  TsuChiang Lei³  Hsin-Ping Wang³ 

[1] Department of Information Technology, Ling Tung University, Taichung 40851, Taiwan;Department of Tourism and Leisure, National Penghu University, Magong 80011 Taiwan;Department of Urban Planning and Spatial Information, Feng Chia University, Taichung 40724, Taiwan;

关键词: data fusion;    ensemble learning;    fuzzy theory;    Dempster-Shafer evidence theory;   

DOI  :  10.3390/rs12213666

来源: DOAJ

【 摘 要 】

Remote sensing technology has rendered lots of information in agriculture. It has usually been used to monitor paddy growing ecosystems in the past few decades. However, there are uncertainties in data fusion techniques which can be resolved in image classification on paddy rice. In this study, a series of learning concepts integrated by a probability progress Fuzzy Dempster-Shafer (FDS) analysis is presented to upgrade various models and different types of image data which is the goal of this study. More specifically, the study utilized the FDS to generate a series of probability models in the classification of the system. In addition, Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) approaches are employed into the developed FDS system. Furthermore, two different image types are Satellite Image and Aerial Photo used as the analysis material. The overall classification accuracy has been improved to 97.27%, and the kappa value is 0.93. The overall accuracy of the paddy field image classification for a multi-period of mid-scale satellite images is between 85% and 90%. The overall accuracy of the classification using multi-spectral numerical aerial photos can be between 91% and 95%. The FDS improves the accuracy of the above image classification results.

【 授权许可】

Unknown