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

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%.

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

“…It has been proved their abilities to optimize model cognition of change and pseudo change. In addition, although the emerging DT frameworks including XGBoost [95] and lightGBM [96] are not widely applied in RS tasks, their potential on urban change detection cannot be ignored. However, even if the forest models show an excellent learning capacity for annotated data, it cannot deal with noise adequately.…”

An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

“…As an ensemble tree model, XGB uses iterative gradient boosters to construct a strong classification model. XGB has shown predictive abilities for binary classification problems [ 57 , 58 ] and land-cover classification problems [ 39 , 47 , 59 , 60 ]. The gradient correction of XGB helps classifier learning and constant estimation from imperfect representations of limited samples.…”

Discriminating the Severity of Basal Stem Rot Disease in Oil Palm (Elaeis guineensis Jacq.) Plantation Using Sentinel-2

“…Managing large plantations is a challenge in terms of monitoring schemes, therefore, the use of remote sensing technology is an efficient and effective strategy to collect plantationrelated data. Plantation sector has increasingly utilized remote sensing technology; details are presented elsewhere, for instance [1][2][3]. This also applies to oil palm plantations.…”