Deep Spectral Clustering With Regularized Linear Embedding for Hyperspectral Image Clustering

The past decade has witnessed the rapid development of deep learning techniques, especially for large-scale and complex data sets. However, it is still a noteworthy problem in dealing with unsupervised hyperspectral image segmentation since inefficiency and misleading result from the absence of supervised information. Generally, spectral clustering is one of the most powerful clustering algorithms, as it often outperforms other methods for image segmentation. Unfortunately, the poor scalability and generalization severely limit the use of spectral clustering, especially for large-scale and high-dimensional hyperspectral images processing. The major motivation of this work is to solve this problem, and we designed a novel algorithm, termed Deep Spectral Clustering with Regularized Linear Embedding (DSCRLE), to benefit from both spectral graph theory and deep learning techniques. The brief procedure is first to construct a fully connected neural network to extract latent feature representations, and then normalize the feature representations by the spectral orthonormal constraint. Lastly, by introducing low-dimensional embedding, we refined the final outputs of all given unlabeled hyperspectral pixels. Extensive experiments have demonstrated that the competitiveness of the proposed method, and it outperforms state-of-art clustering approaches in the task of hyperspectral image segmentation.