Abstract Hyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention in the deep learning field recently. Nevertheless, during NN training, the overfitting problem may cause continuous dragging of the algorithm toward larger error. In this case, a regularization technique is needed to constitute the most useful decision boundary. The Eigenvalue Decay method is one of the regularization techniques that may be applied for HSI. This study investigates the performance of Multilayer Perceptron trained with an Eigenvalue Decay (MLP-ED) algorithm for HSI classification. The SVM, CNN with Pixel-Pair and CNN-Ensemble methods are used as comparison algorithms for MLP-ED performance assessment. All methods were tested with 3 different high-resolution HSI datasets. While SVM is one of the classic classifiers, and the 2 new CNN algorithms show high performance, the proposed MLP-ED method has more computational efficiency and achieves higher success than the others do.

中文翻译：

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基于特征值衰减训练的多层感知器的高光谱图像分类

摘要高光谱图像 (HSI) 需要足够的标记样本和复杂的分类器来识别区域。支持向量机 (SVM) 是该领域最有竞争力的算法之一。神经网络（NN）是另一种用于分类问题的方法，两者都在文献中被广泛提出。卷积神经网络（CNN）方法最近在深度学习领域也受到了极大的关注。尽管如此，在神经网络训练过程中，过拟合问题可能会导致算法不断向更大的误差方向拖动。在这种情况下，需要一种正则化技术来构成最有用的决策边界。特征值衰减方法是可以应用于 HSI 的正则化技术之一。本研究调查了使用特征值衰减 (MLP-ED) 算法训练的多层感知器的性能，用于 HSI 分类。SVM、CNN with Pixel-Pair 和 CNN-Ensemble 方法被用作 MLP-ED 性能评估的比较算法。所有方法都使用 3 个不同的高分辨率 HSI 数据集进行了测试。虽然 SVM 是经典的分类器之一，并且 2 种新的 CNN 算法表现出高性能，但所提出的 MLP-ED 方法比其他方法具有更高的计算效率和更高的成功率。