Next Article in Journal
Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China
Next Article in Special Issue
Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties
Previous Article in Journal
Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM)
Previous Article in Special Issue
Diurnal Cycle in Atmospheric Water over Switzerland
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(9), 921;

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
National Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, Korea
Author to whom correspondence should be addressed.
Received: 11 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 2 September 2017
(This article belongs to the Special Issue Earth Observations for Addressing Global Challenges)
Full-Text   |   PDF [1827 KB, uploaded 4 September 2017]   |  


To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels. View Full-Text
Keywords: classification; self-learning; training data; crop classification; self-learning; training data; crop

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).

Share & Cite This Article

MDPI and ACS Style

Kim, Y.; Park, N.-W.; Lee, K.-D. Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps. Remote Sens. 2017, 9, 921.

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



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