Xu Shenyuan, Liu Size, Wang Hua, Chen Wenjie, Zhang Fan, Xiao Zhu
State Key Laboratory of Geo-Information Engineering, Xi'an 710054, China.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Entropy (Basel). 2020 Dec 25;23(1):20. doi: 10.3390/e23010020.
At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may lead to an overfitting of the model, especially when training data are insufficient. As the performance of the model mainly depends on sufficient data and a large network with reasonably optimized hyperparameters, using DNNs for classification requires better hardware conditions and sufficient training time. This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral features. Furthermore, a multi-layered gradient boosting decision tree model is constructed for classification. We conduct experiments based on three datasets, which in this paper are referred to as the Pavia University, Indiana Pines, and Salinas datasets. It is shown that the proposed FF-DT achieves better performance in classification accuracy, training conditions, and time consumption than other current classical hyperspectral image classification methods.
目前，许多深度神经网络（DNN）方法已被广泛用于高光谱图像分类。利用此类模型已取得了有前景的分类结果。然而，由于模型的复杂性和深度，增加模型参数数量可能会导致模型过拟合，尤其是在训练数据不足时。由于模型的性能主要取决于充足的数据以及具有合理优化超参数的大型网络，使用DNN进行分类需要更好的硬件条件和充足的训练时间。本文提出了一种用于高光谱图像分类的特征融合与多层梯度提升决策树模型（FF-DT）。首先，我们将扩展形态学轮廓（EMP）、线性多尺度空间特征和非线性多尺度空间特征融合为最终特征，以提取空间特征和光谱特征。此外，构建了一个多层梯度提升决策树模型用于分类。我们基于三个数据集进行实验，在本文中分别称为帕维亚大学、印第安纳松树和萨利纳斯数据集。结果表明，所提出的FF-DT在分类准确率、训练条件和时间消耗方面比其他当前经典的高光谱图像分类方法具有更好的性能。
