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

Dyah R Panuju,Bambang H Trisasongko,David J Paull

doi:10.3390/rs11010100

Dyah R Panuju, Bambang H Trisasongko + Show 1 more

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https://doi.org/10.3390/rs11010100

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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%.

Highlights

Monitoring of change over conserved areas may not always identify significant differences between two observations
Multivariate Alteration Detection (MAD) variate-1 yielded a similar indication for location-1, but it failed to indicate any changes in location-2
The second MAD variate produced from Iteratively Reweighted Multivariate Alteration Detection (IRMAD) processing filtered with Gamma Map successfully indicated the location of change

Summary

Introduction

Monitoring of change over conserved areas may not always identify significant differences between two observations. Clouds limit the capability of monitoring by using optical images for areas covered with clouds, which can remain unobservable for days, weeks or months at a time [4,5,6]. In these instances, successful change detection is not guaranteed through the exclusive use of optical imagery such as Landsat or other sensors [7]. Successful change detection is not guaranteed through the exclusive use of optical imagery such as Landsat or other sensors [7] Microwave remote sensing such as synthetic aperture radar (SAR) is an option for monitoring remote areas that are severely affected by clouds. Change detection (CD) of SAR data has been implemented over urban [9,10], agricultural [11] or forested [12,13] areas

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