Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification often faces the problem of multi-class imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning (DL) has been successfully applied to HSI classification, convolutional neural network (CNN) is one of the most representative of them. However, it is difficult to effectively improve the accuracy of minority classes under the problem of multi-class imbalance. In addition, ensemble learning has been successfully applied to solve multi-class imbalance, such as random forest (RF). This paper proposes a novel ERFS (enhanced random feature subspace)-based ensemble CNN algorithm for the multi-class imbalanced problem. The main idea is to perform random oversampling (ROS) of training samples and multiple data enhancements based on random feature subspace (RFS), and then construct an ensemble learning model combining random feature selection and CNN to HSI classification. Experimental results on three public hyperspectral datasets show that the performance of the proposed method is better than traditional CNN, RF, and deep learning ensemble methods. Index Terms-Hyperspectral image (HSI) classification, multiclass imbalance, enhanced random feature subspace (ERFS), ensemble learning, convolutional neural network (CNN).

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

“…Therefore, a class imbalance issue leads in (multiclass) HS classification to decreased accuracy of many standard algorithms such as decision trees, k-nears neighbor, neural networks, and SVM [218]. Especially for high-dimensional data (HS) and ML/DL-based multiclass problems, the minority classes are neglected or misclassified [219]. Various strategies can be applied to overcome imbalance class issues partially: simplification of the network architecture [38], data augmentation for minority classes, and random sampling for equal class distribution [220].…”

A Novel Hybrid Learning System Using Modified Breaking Ties Algorithm and Multinomial Logistic Regression for Classification and Segmentation of Hyperspectral Images

“…The multi-level logistic (MLL) algorithm was then employed with Markov random fields (MRF) to compute the segmentation [44,45]. Moreover, MRF works on the model of Gibbs energy to calculate the best segmentation label, based on current pixel potential for the neighboring pixels, to decide whether this pixel belongs to the current region or not [46,47].…”

“…There exist numerous methods for the use of large ground truth information such as SVM [39], semisupervised SVM [40], multinomial logistic regression (MLR) [41], partial least squares regression [42], and artificial neural networks [43]. In both spectral and spatial information, researchers have started to employ neural networks such as densely connected multiscale attention networks [44], end-to-end fully convolutional segmentation networks [45], and convolutional neural networks (CNN) with random forest [46] and Ghostnet [47].…”

Depthwise Separable Relation Network for Small Sample Hyperspectral Image Classification

“…The purpose of the deep separable embedding model is to obtain high-quality feature vectors, so as to splice high-quality symmetric feature spaces with comparative metric models. In deep learning, convolutional neural network is widely used in the field of image classification because of its strong feature expression ability [34]. The convolutional neural network obtains the information of different feature maps at different positions through neurons, so that the obtained images have richer features.…”