HCEC: An effective hybrid CNN-ensemble classifier for hyperspectral image classification

Hyperspectral image classification (HSI) is extensively utilized to analyse remotely sensed images for different real-world applications. Recently, convolutional neural network(CNN) have been applied successfully for HSI classification. Ensemble learning methods such as adaptive boosting (AdaBoost), random forest(RF), and extreme gradient boosting (XGBoost) are comparatively simpler techniques that also achieve a significant level of performance for HSI classification. This article proposes a hybrid classifier for HSI integrating the merits of two prominent classifiers: CNN and ensemble learning method. Both of them have evidence of efficient recognition capability of finding patterns from data. The ensemble model performs recognition using the salient features extracted by CNN. A modified version of AlexNet compatible with HSI datacube has been adopted as the CNN model. Three ensemble methods have been applied for recognition: AdaBoost, RF, and XGBoost. Two base learners have been used with AdaBoost: decision tree and Support Vector Machine(SVM). Experiments have been performed on three benchmark datasets: Indian Pines(IN), University of Pavia(UP) and Kennedy Space Centre(KSC), which mostly include land cover of agriculture, forest, soil, rural, and urban area. Experimental results establish the superiority of CNN with Adaboost hybrid method where SVM is used as the base learner. It delivers maximum overall accuracy (OA) of 91.48%, 99.18%, and 88.10% and kappa score of 90.25%, 98.91%, and 86.71%, for IN, UP and KSC dataset, respectively.