嵌入式深度神经网络高光谱图像聚类

Objective: Hyperspectral remote sensing, which is also called imaging spectral remote sensing, is a combined imaging and spectroscopy of multi-dimensional information retrieval technology. It carries abundant spectral information and is widely used in earth observation. A hyperspectral image is a kind of nonlinear structured data with a high dimension, and it poses a great challenge to the clustering task. If direct processing of the spectral information of hyperspectral images requires a large amount of computation, then appropriate dimensionality reduction methods for the nonlinear structure of hyperspectral data must be adopted. Although many clustering methods have been proposed, these traditional methods involve shallow linear models, the efficiency of the similarity measure is low, and the clustering effect is often poor for high-dimensional or hyperspectral data with a nonlinear structure. Traditional clustering algorithms encounter difficulties when clustering high-dimensional data. The concept of subspace clustering has been proposed to solve the problem of high-dimensional data clustering. Subspace clustering can solve the clustering problem of high-dimensional data. However, existing subspace clustering algorithms typically employ shallow models to estimate the underlying subspaces of unlabeled data points and cluster them into corresponding clusters. They have several limitations. First, the clustering effect of these subspace clustering methods depends on the quality of the affinity matrix. Second, due to the linear assumption of the data, these methods cannot deal with data with a nonlinear structure. Several nuclear methods have been proposed to overcome these shortcomings. These methods map the data to a predefined kernel space where they perform subspace clustering. A disadvantage of these nuclear space clustering methods is that their performance depends heavily on the kernel functions used. Existing data transformation methods include linear transformation, such as principal component analysis (PCA), and nonlinear transformation, such as the kernel method. However, data with a highly complex potential structure is still a huge challenge to the effectiveness of existing clustering methods, and most clustering algorithms, such as shallow models, can only extract shallow features. Owing to the limited representation capacity of the employed shallow models, the algorithms may fail in handling realistic data with high-dimensional nonlinear structures. Moreover, most learning approaches treat feature extraction and clustering separately, train the feature extraction model well, and only use the clustering algorithm once in the feature representation of data to obtain clustering results. Method: To solve these problems, the use of spectral information is maximized, and a new subspace clustering algorithm, that is, embedded deep neural network fuzzy c-means clustering (EDFCC), is proposed in this study. The EDFCC algorithm can effectively extract the spectral information of hyperspectral images and be used for hyperspectral image clustering. The fuzzy c-means clustering algorithm is embedded into the deep autoencoder network, and the joint learning deep autoencoder network and fuzzy c-means clustering algorithm are used. Optimizing the two tasks jointly can substantially improve the performance of both. First, the feature extraction process of data is assumed to be an unknown transformation, which may be a nonlinear function. To preserve the local structure, the representation of each data point is learned by minimizing the reconstruction error, that is, the feature extraction process is completed by learning the deep autoencoder network. Data should be clustered in an effective manner to learn the representation of the potential features of data suitable for clustering. The fuzzy c-means clustering algorithm is used to constrain the feature extraction process and make the generated features suitable for clustering. The motivation for designing the EDFCC algorithm is to maintain the advantage of the joint optimization of the two tasks while using the capability of the deep autoencoder network to approximate any nonlinear function, gradually map the input data points to the potential nonlinear space, and adjust the clustering indicator matrix dynamically with the model training. Result: Two hyperspectral data sets, namely, Indian Pines and Pavia University, are used to test the validity of the EDFCC algorithm. The quantitative evaluation metrics include accuracy and normalized mutual information. The Indian Pines dataset contains data acquired by the airborne visible infrared imaging spectrometer with a spectral range of 0.412.45 m, spatial resolution of 25 m, spectral resolution of 10 nm, and a total of 145 × 145 sample points. A total of 220 original bands are available, but the water vapor absorption band and bands with a low signal-to-noise ratio are excluded. The remaining 200 bands are used as research objects. The Indian Pines dataset has 16 different feature categories. Indian Pines shows that the overall clustering accuracy of the EDFCC algorithm is 42.95%, which is 3% higher than that of the best LRSC algorithm. The Pavia University dataset was obtained by the airborne reflector optical spectral imager in Germany. Its spectral range is 0.430.86 m, and its spatial resolution is 1.3 m. The dataset contains 610×340 sample points. A total of 115 original bands exist, but the noise bands are removed. The 103 remaining bands are used as research objects. The Pavia University dataset has nine types of ground objects. The dataset shows that the overall clustering accuracy of the EDFCC algorithm is 60.59%, which is 4% higher than that of the best LRSC algorithm. When compared with the AEKM algorithm for deep clustering, the AEKM algorithm is improved by 2% and 3%. Conclusion: The EDFCC algorithm is proposed in this study. The algorithm is first applied in hyperspectral image clustering as a joint learning framework. The indicator matrix can be dynamically adjusted because of joint learning, and no additional training process is required, which greatly improves the training efficiency. Experimental results show that the EDFCC algorithm can extract many effective deep features from the high-dimensional spectral information of hyperspectral images and improve clustering accuracy.