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.

中文翻译：

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基于增强随机特征子空间的集成CNN用于不平衡高光谱图像分类

高光谱图像（HSI）分类经常面临多类不平衡的问题，这被认为是遥感领域的主要挑战之一。近年来，深度学习已成功地应用于HSI分类中，卷积神经网络（CNN）是其中最具代表性的一种。但是，在多阶级不平衡的问题下，很难有效地提高少数民族阶级的准确性。此外，集成学习已成功应用于解决多类不平衡问题，例如随机森林（RF）。本文针对多类不平衡问题提出了一种基于增强型随机特征子空间的集成CNN算法。主要思想是基于随机特征子空间对训练样本进行随机过采样并进行多种数据增强，然后，构建结合随机特征选择和CNN到HSI分类的集成学习模型。在三个公共高光谱数据集上的实验结果表明，该方法的性能优于传统的CNN，RF和深度学习集成方法。