传统的高光谱图像分类大多只考虑地物的光谱信息,而忽略了空间信息,现有的空谱联合分类方法难以有效提取空间邻域信息。针对上述问题,提出了一种卷积神经网络和稀疏字典联合学习的算法。现有稀疏编码方法大多仅考虑光谱信息而丢弃空间信息,而所提算法利用卷积神经网络可以有效提取数据深层特征的优势,对高光谱图像同时提取空谱特征,获取高维深层特征;然后再通过字典学习对深层特征进行稀疏编码,以获取用于分类的鉴别特征;最后采用分类器确定分类结果。在实验部分,采用所提算法与现有几种算法在三个公开数据集上进行分类,结果发现:相比于其他算法,所提算法的总体分类精度、平均分类精度和Kappa系数均有所提升。所提方法可以同时提取高光谱数据的空谱特征,具有良好的鲁棒性和判别性,有效地提高了分类的准确率,并且在样本数量小的数据集上表现良好。

Conventional hyperspectral image classification only considers ground objects' spectral information and ignores the spatial information. The existing space-spectrum joint classification methods are difficult to effectively extract the spatial neighborhood information. To address these problems, this paper proposes a method that combines a convolutional neural network with a sparse dictionary. Most existing sparse coding methods only consider the spectral information and discard the spatial information. The proposed method leverages the advantages of a convolutional neural network to effectively extract the deep data features, simultaneously extracts the spatial-spectral features of hyperspectral images, obtains the high-dimensional deep features, and then applies sparse coding to the deep features through dictionary learning to obtain the identification features for classification. The classification results are confirmed using the classifier. Experiments are conducted in which three open datasets are classified using the proposed method and five existing algorithms. The proposed method outperforms the other methods in terms of overall classification accuracy, average classification accuracy, and the Kappa coefficient. The experimental results demonstrate that the proposed method can simultaneously extract the spatial-spectral features of hyperspectral data, has good robustness and discrimination, effectively improves classification accuracy, and performs well on a dataset with a small number of samples.