###### Abstract  
The classification of hyperspectral data using deep learning methods can obtain better results than the previous shallow classifiers, but deep learning algorithms have some limitations. These algorithms require a large amount of data to train the network, while also needing a certain amount of labeled data to fine-tune the network. In this paper, we propose a new hyperspectral data processing method based on transfer learning and the deep learning method. First, we use a hyperspectral data set that is similar to the target data set to pre-train the deep learning network. Then, we use the transfer learning method to find the common features of the source domain data and target domain data. Second, we propose a model structure that combines the deep transfer learning model to utilize a combination of spatial information and spectral information. Using transfer learning, we can obtain the spectral features. Then, we obtain several principal components of the target data. These will be regarded as the spatial features of the target domain data, and we use the joint features for the classifier. The data are obtained from a hyperspectral public database. Using the same amount of data, our method based on transfer learning and deep belief network obtains better classification accuracy in a shorter amount of time. hyperspectral data; deep learning; transfer learning; DBN +
Footnote †: journal: Medical  
and identification of hyperspectral data, calibration of the corresponding artificial labels will result in a large amount of manpower and a loss of financial resources [10]. There are many research methods to address dimensionality disasters and other issues at the early stages. For feature selection and feature extraction in the hyperspectral data spectral dimension, in the earliest research on the feature extraction of hyperspectral data, the main research methods, such as principal component analysis (PCA) [11], independent component analysis (ICA) [12], and linear discriminant analysis [13], all use linear models to extract features. However, hyperspectral data are non-linear. Therefore, in 2006, [PERSON] improved deep neural networks and solved the problem of gradient diffusion and the local extreme value of deep neural networks by means of layer-by-layer greedy training [14]. Afterwards, deep learning networks were gradually applied in a series of feature extraction and classification approaches to hyperspectral data. Among these approaches are the deep belief network (DBN) [15], stacked autoencoding (SAE) [16], denoising autoencoding (DAE) [17], and conventional neural networks (CNNs) [18; 19]. The applications of these deep learning networks (with the adjustment of the number of network layers and the number of neuron nodes) can yield a series of features of hyperspectral data from low levels to abstraction [20]. While the deep learning network structure can obtain very effective features, a deep learning network conducts training for a long time; thus, it takes a large amount of manpower and financial resources to sample the data calibration [21]. However, if unsupervised learning is used for the network training, then there are disadvantages, such as low classification accuracy [22]. This paper proposes a novel deep learning network model that uses the transfer learning approach. A part of the model that has been trained in the hyperspectral data in one field is proposed. For a new field of hyperspectral data, if there is some connection or similarity between the hyperspectral data of these two fields, then we do not need as much data to study the hyperspectral data set of the new field. We would only need a small amount of data to complete the network training. Therefore, based on the idea of transfer learning, we can realize that the new model requires a very small amount of sample data, and does not need to repeatedly train all of the deep learning networks.