Next Article in Journal
Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks
Next Article in Special Issue
Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016
Previous Article in Journal
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Previous Article in Special Issue
Use of High-Quality and Common Commercial Mirrors for Scanning Close-Range Surfaces Using 3D Laser Scanners: A Laboratory Experiment
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1163; https://doi.org/10.3390/rs9111163

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

1
Department of Smart ICT Convergence, Konkuk University, Seoul 05029, Korea
2
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
3
Department of Advanced Technology Fusion, Konkuk University, Seoul 05029, Korea
4
Agency for Defense Development, Daejeon 34060, Korea
*
Author to whom correspondence should be addressed.
Received: 21 July 2017 / Revised: 10 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
Full-Text   |   PDF [7391 KB, uploaded 13 November 2017]   |  

Abstract

Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth’s surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets. View Full-Text
Keywords: random forest regression; relative radiometric normalization; nonlinear; scattergram; change detection random forest regression; relative radiometric normalization; nonlinear; scattergram; change detection
Figures

Figure 1

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

Seo, D.K.; Kim, Y.H.; Eo, Y.D.; Park, W.Y.; Park, H.C. Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection. Remote Sens. 2017, 9, 1163.

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