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

Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning has been successfully applied to the HSI classification, a 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 multiclass imbalance. In addition, ensemble learning has been successfully applied to solve multiclass imbalance, such as random forest (RF) This article proposes a novel enhanced-random-feature-subspace-based ensemble CNN algorithm for the multiclass imbalanced problem. The main idea is to perform random oversampling of training samples and multiple data enhancements based on random feature subspace, and then, construct an ensemble learning model combining random feature selection and CNN to the HSI classification. Experimental results on three public hyperspectral datasets show that the performance of the proposed method is better than the traditional CNN, RF, and deep learning ensemble methods.

Automated summary

The article proposes a novel enhanced-random-feature-subspace-based ensemble CNN algorithm for multiclass imbalanced problem. By performing random oversampling and data enhancements based on random feature subspace, the algorithm combines random feature selection and CNN for HSI classification, showing better performance than traditional CNN, RF, and deep learning ensemble methods in experimental results on three public hyperspectral datasets.