Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images

Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the sub-manifolds information of the original dataset, multimanifold learning is effective in enhancing the discriminant ability of the processed dataset. In addition, due to the ability of preserving the spatial neighborhood structure information, tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above analysis, a novel tensorbased low rank graph with multi-manifold regularization (TLGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In T-LGMR, a low rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches