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

Tsu Chiang Lei,Shih-Chieh Wu,Hsin-Ping Wang,Shiuan Wan

doi:10.3390/rs12213666

Tsu Chiang Lei, Shih-Chieh Wu + Show 2 more

Open Access

https://doi.org/10.3390/rs12213666

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Journal: Remote sensing	Publication Date: Nov 9, 2020
Citations: 6	License type: CC BY 4.0

Affiliation: Feng Chia University, Ling Tung University

Abstract

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.

Highlights

Paddy rice is a major food source for more than half of the world’s population, mostly in regions of Asia, Africa, and Latin America
The paddy area and non-paddy area patch samples are randomly selected for 3193 paddy area samples and 8783 non-paddy area samples in the training process
This study develops a set of decision-making mechanisms for the uncertainty paddy area data generated by the above analysis and further extracts the classification rate value of each algorithm with producing high information uncertainty

Summary

Introduction

Paddy rice is a major food source for more than half of the world’s population, mostly in regions of Asia, Africa, and Latin America. A solution is requested to determine the best image source or classification method to produce the best paddy area thematic maps This concept obviously cannot correctly solve the problem of a complex world [22]. These three classifiers are statistical “Logistic Regression (LR)” and “Support Vector Machine (SVM)” for machine learning, and “Artificial Neural Network (ANN)” in the field of artificial intelligence Each of these three classifiers has its advantages and disadvantages, and we will produce different results for this paddy area classification issue, so we use the concept of Dempster-Shafer (DS), through the practice of an evidence synthesis (Evidential reasoning, ER) algorithm, the three pieces of classification information are integrated into a piece of single decision-making information, thereby improving the uncertainty of the classification results, using the method of the FDS (Fuzzy-DS theory) theory of evidence.

Logistic Regression

Support Vector Machine

Artificial Intelligence

Information Processing Architecture of Multiple Classifiers

Classification Results of Aerial Photo Image

The Results of Feature Classification

Establishment of the Results of the Decision-Making Interpretation Module