Learning Hierarchical Spectral–Spatial Features for Hyperspectral Image Classification
Learning Hierarchical Spectral–Spatial Features for Hyperspectral Image Classification
复制标题
DOI：
10.1109/tcyb.2015.2453359
复制
发表时间：
2016-07
影响因子：
11.8
通讯作者：
Yicong Zhou;Yantao Wei
中科院分区：
文献类型：
--
作者：
Yicong Zhou;Yantao Wei
提出了一种基于光谱-空间特征学习的高光谱图像鲁棒特征提取方法。它结合了光谱特征学习和空间特征学习在一个层次的方式。在此基础上，进一步提出了一种基于SSFL单元的光谱空间网络（SSN）的HSI分类模型。SSN可以同时利用区分光谱和空间信息。具体来说，SSN通过在光谱和空间特征学习操作之间交替来学习有用的高级特征。然后，基于核的极端学习机（KELM），一个浅层神经网络，嵌入到SSN分类图像像素。在两个基准HSI数据集上进行了大量的实验，以验证SSN的有效性。与现有方法相比，具有深层结构的SSN在总体分类准确率、平均分类准确率和一致性系数等方面均具有较高的分类准确率，尤其是在训练样本较少的情况下。
This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral-spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa (κ) coefficient of agreement, especially when the number of the training samples is small.