Next Article in Journal
Normalized Difference Vegetation Index as an Estimator for Abundance and Quality of Avian Herbivore Forage in Arctic Alaska
Previous Article in Journal
High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1233; https://doi.org/10.3390/rs9121233

Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields

1
School of Electronic Information, Wuhan University, Wuhan 430072, China
2
The CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China
3
College of Information Engineer, Inner Mongolia University of Technology, Hohhot 101051, China
*
Author to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 25 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [14395 KB, uploaded 29 November 2017]   |  

Abstract

Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data labelling tasks are needed, and the sample annotation process with real categories is tedious and costly. To relieve the burden of labelling and to obtain satisfactory results, we propose an interactive change detection framework based on active learning and Markov random field (MRF). More specifically, a limited number of representative objects are found in an unsupervised way at the beginning. Then, the very limited samples are labelled as “change” or “no change” to train a simple binary classification model, i.e., a Gaussian process model. By using this model, we then select and label the most informative samples by “the easiest” sample selection strategy to update the former weak classification model until the detection results do not change notably. Finally, the maximum a posteriori (MAP) change detection is efficiently computed via the min-cut-based integer optimization algorithm. The time consuming and laborious manual labelling process can be reduced substantially, and a desirable detection result can be obtained. The experiments on several WorldView-2 images demonstrate the effectiveness of the proposed method. View Full-Text
Keywords: high resolution remote sensing images; change detection; active learning; gaussian processes; Markov random fields high resolution remote sensing images; change detection; active learning; gaussian processes; Markov random fields
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

Yu, H.; Yang, W.; Hua, G.; Ru, H.; Huang, P. Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields. Remote Sens. 2017, 9, 1233.

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