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Remote Sens. 2019, 11(1), 100; https://doi.org/10.3390/rs11010100

Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes

1
School of Physical, Environmental and Mathematical Sciences, UNSW Canberra, Northcott Drive, Campbell, ACT 2600, Australia
2
Department of Soil Science and Land Resource, Bogor Agricultural University, Jalan Meranti, Kampus IPB Darmaga, Bogor 16680, Indonesia
*
Author to whom correspondence should be addressed.
Received: 29 November 2018 / Revised: 3 January 2019 / Accepted: 4 January 2019 / Published: 8 January 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for post-classification change analysis. The accuracy of change detection was iteratively evaluated based on thresholds composed of mean and a range constant of standard deviation. Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected texture features. Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN), Bagged Multivariate Adaptive Regression Spline (MAB), Regularised Random Forest (RFG), Original Random Forest (RFO), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGB). The best accuracy was 98.8%, which resulted from thresholding MAD variate-2 with constants at 1.7. The highest improvement of classification accuracy was obtained by amending the grey level co-occurrence matrix (GLCM) texture. The identification of variable importance (VI) confirmed that selected GLCM textures (mean and variance of HH or HV) were equally superior, while the contribution of index and decomposition were negligible. The best model produced similar classification accuracy at about 90% for both years 2007 and 2010. Tree-based algorithms including RFO, RFG and XGB were more robust than SVM and ENN. Subtle changes indicated by binary change analysis were somewhat hidden in post-classification analysis. Reclassification by combining all important variables and adding five classes to include subtle changes assisted by Google Earth yielded an accuracy of 82%. View Full-Text
Keywords: binary change analysis; change detection; Halimun Salak National Park; IRMAD; post-classification; Random Forest; SAR backscatter; synthetic data; subtle change binary change analysis; change detection; Halimun Salak National Park; IRMAD; post-classification; Random Forest; SAR backscatter; synthetic data; subtle change
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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).
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Panuju, D.R.; Paull, D.J.; Trisasongko, B.H. Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes. Remote Sens. 2019, 11, 100.

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