Abstract
To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.
Highlights
Hyperspectral images can provide detailed spectral information, thereby increasing the possibility of accurately discriminating materials of interest
Support vector machines (SVMs) have shown remarkable performance in terms of classification accuracy in scenarios with a limited number of labeled samples available [5,6], and high performance, generalization, prediction accuracy and operation speed characteristics have been observed for random forest (RaF), rotation forest (RoF), extreme learning machine (ELM), extremely randomized decision trees (ExtraTrees), classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) classifiers in many studies [7,8,9,10,11]; these approaches encompass multispectral to hyperspectral methods and are applicable for optical images to synthetic aperture radar (SAR) and polarimetric SAR (PolSAR) images [7,8,10,12,13,14,15,16]
The main contributions of this article are as follows: (1) XGBoost was introduced and investigated for spectral-spatial hyperspectral image classification; (2) extended maximally stable extreme-region-guided morphological profiles were proposed for spatial feature extraction from hyperspectral images; and (3) meta-XGBoost was proposed as an ensemble of different boosters with few and simple parameters
Summary
Hyperspectral images can provide detailed spectral information, thereby increasing the possibility of accurately discriminating materials of interest. As a boosting algorithm, XGBoost with a CART booster can be influenced by the well-known overfitting problem in the context of boosting, and this issue affects multiple additive regression trees (MARTs) [27,41]. This problem occurs when few trees are available at early iterations; as a result, these trees all make large contributions to the model. The main contributions of this article are as follows: (1) XGBoost was introduced and investigated for spectral-spatial hyperspectral image classification; (2) extended maximally stable extreme-region-guided morphological profiles were proposed for spatial feature extraction from hyperspectral images; and (3) meta-XGBoost was proposed as an ensemble of different boosters with few and simple parameters. Very high resolution Indian remote sensing satelllite Linear image self scanning system III
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