Next Article in Journal
Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network
Next Article in Special Issue
Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States
Previous Article in Journal
A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds
Previous Article in Special Issue
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(3), 298; https://doi.org/10.3390/rs11030298

Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

1,2,3,4
,
1,3,4
,
1,3,4,*
,
1,3,4
,
5
,
1,2,3,4
and
1,6
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
4
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
5
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
6
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 25 January 2019 / Accepted: 30 January 2019 / Published: 1 February 2019
Full-Text   |   PDF [3382 KB, uploaded 1 February 2019]   |  

Abstract

In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples. View Full-Text
Keywords: remote sensing; wheat powdery mildew; monitoring; transfer learning; TrAdaBoost remote sensing; wheat powdery mildew; monitoring; transfer learning; TrAdaBoost
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, L.; Dong, Y.; Huang, W.; Du, X.; Luo, J.; Shi, Y.; Ma, H. Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method. Remote Sens. 2019, 11, 298.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top