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Small‐sample‐size problem is always a challenge for hyperspectral image (HSI) classification. Considering the co‐occurrence of land‐cover classes between similar scenes, transfer learning can be performed, and cross‐scene classification is deemed a feasible approach proposed in recent years. In cross‐scene classification, the source scene which possesses sufficient labelled samples is used for assisting the classification of the target scene that has a few labelled samples. In most situations, different HSI scenes are imaged by different sensors resulting in their various input feature dimensions (i.e. number of bands), hence heterogeneous transfer learning is desired. An end‐to‐end heterogeneous transfer learning algorithm namely domain‐invariant attention network (DIAN) is proposed to solve the cross‐scene classification problem. The DIAN mainly contains two modules. (1) A feature‐alignment CNN (FACNN) is applied to extract features from source and target scenes, respectively, aiming at projecting the heterogeneous features from two scenes into a shared low‐dimensional subspace. (2) A domain‐invariant attention block is developed to gain cross‐domain consistency with a specially designed class‐specific domain‐invariance loss, thus further eliminating the domain shift. The experiments on two different cross‐scene HSI datasets show that the proposed DIAN achieves satisfying classification results. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model is always important to tackle this problem because of its highly ill-posed nature. In this article, we introduce a linear spectral mixture model (LMM)-based end-to-end deep neural network named SNMF-Net for hyperspectral unmixing. SNMF-Net shares an alternating architecture and benefits from both model-based methods and learning-based methods. On the one hand, SNMF-Net is of high physical interpretability as it is built by unrolling $L_{p}$ sparsity constrained nonnegative matrix factorization ($L_{p}$ -NMF) model belonging to LMM families. On the other hand, all the parameters and submodules of SNMF-Net can be seamlessly linked with the alternating optimization algorithm of $L_{p}$ -NMF and unmixing problem. This enables us to reasonably integrate the prior knowledge on unmixing, the optimization algorithm, and the sparse representation theory into the network for robust learning, so as to improve unmixing. Experimental results on the synthetic and real-world data show the advantages of the proposed SNMF-Net over many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e.g., background clutter and rapid changes of target appearance. Alternatively, material information of targets contained in large amount of bands of hyperspectral images (HSI) is more robust to these difficult conditions. In this paper, we conduct a comprehensive study on how material information can be utilized to boost object tracking from three aspects: dataset, material feature representation and material based tracking. In terms of dataset, we construct a dataset of fully-annotated videos, which contain both hyperspectral and color sequences of the same scene. Material information is represented by spectral-spatial histogram of multidimensional gradients, which describes the 3D local spectral-spatial structure in an HSI, and fractional abundances of constituted material components which encode the underlying material distribution. These two types of features are embedded into correlation filters, yielding material based tracking. Experimental results on the collected dataset show the potentials and advantages of material based object tracking. [ABSTRACT FROM AUTHOR]

Due to the mechanism of the data acquisition process, hyperspectral imagery (HSI) are usually contaminated by various noises, e.g., Gaussian noise, impulse noise, strips, and dead lines. In this article, a spectral–spatial $L_{0}$ gradient regularized low-rank tensor factorization (LRTF $L_{0}$) method is proposed for hyperspectral denoising, in which the restored HSI is approximated by low-rank block term decomposition (BTD). BTD factorizes a tensor into the sum of a series of component tensors, each of which is represented by the outer product of a matrix and a vector. From subspace learning point of view, the vector and matrix can be considered as a spectral atom and its corresponding coding coefficients. In the proposed method, the correlations in both spectral and spatial domains are taken into account via the small size of atom set and low-rankness of coding matrices. In addition, HSIs also have the local structure of piecewise smoothness in both spectral and spatial domains. Motivated by the supreme virtues of $L_{0}$ gradient regularization in image structure exploitation, we develop a spectral–spatial $L_{0}$ gradient regularization and embed it into BTD to explore the spectral–spatial texture information. The proposed method can simultaneously remove various types of noises, and the experimental results on both synthetic data and real-world data show its superiority when compared with several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing decomposes a hyperspectral imagery (HSI) into a number of constituent materials and associated proportions. Recently, nonnegative tensor factorization (NTF)-based methods have been proposed for hyperspectral unmixing thanks to their capability in representing an HSI without any information loss. However, tensor factorization-based HSI processing approaches often suffer from low-signal-to-noise ratio condition of HSI and nonuniqueness of the solution. This problem can be effectively alleviated by introducing various spatial constraints into tensor factorization to suppress the noise and decrease the number of extreme, stationary, and saddle points. On the other hand, total variation (TV) adaptively promotes piecewise smoothness while preserving edges. In this paper, we propose a TV regularized matrix–vector NTF method. It takes advantage of tensor factorization in preserving global spectral–spatial information and the merits of TV in exploiting local spatial information, thus generating smooth abundance maps with preserved edges. Experimental results on synthetic and real-world data show that the proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Many spectral unmixing approaches ranging from geometry, algebra to statistics have been proposed, in which nonnegative matrix factorization (NMF)-based ones form an important family. The original NMF-based unmixing algorithm loses the spectral and spatial information between mixed pixels when stacking the spectral responses of the pixels into an observed matrix. Therefore, various constrained NMF methods are developed to impose spectral structure, spatial structure, and spectral-spatial joint structure into NMF to enforce the estimated endmembers and abundances preserve these structures. Compared with matrix format, the third-order tensor is more natural to represent a hyperspectral data cube as a whole, by which the intrinsic structure of hyperspectral imagery can be losslessly retained. Extended from NMF-based methods, a matrix-vector nonnegative tensor factorization (NTF) model is proposed in this paper for spectral unmixing. Different from widely used tensor factorization models, such as canonical polyadic decomposition CPD) and Tucker decomposition, the proposed method is derived from block term decomposition, which is a combination of CPD and Tucker decomposition. This leads to a more flexible frame to model various application-dependent problems. The matrix-vector NTF decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a vector (endmember) and a matrix (corresponding abundances). From a formal perspective, this tensor decomposition is consistent with linear spectral mixture model. From an informative perspective, the structures within spatial domain, within spectral domain, and cross spectral-spatial domain are retreated interdependently. Experiments demonstrate that the proposed method has outperformed several state-of-the-art NMF-based unmixing methods. [ABSTRACT FROM AUTHOR]

• We design an undersampling rapid coded aperture spectrometer system. This system refers to the spatial information of RGB to select the sampling order and uses the DCT matrix as the coding patterns. The designed imaging system can better address the contradiction between image resolution and imaging speed to a certain extent, so as to achieve high speed and high resolution hyperspectral imaging. • A spectral image enhancement system is designed to improve the image spatial detail recognition capability that consists of a CMMI information fusion function and a PMS-net-based feature extraction module. By improving accuracy at a lower cost, the network structure is made simpler, which improves the generalization and robustness of the network to some extent. • Based on the fast conversion of the micromirror unit in DMD, we built a DMD-based hyperspectral imaging system. We obtained real hyperspectral datasets in reality by this system, and verified that our proposed algorithm has good performance on real spectral datasets. In recent years, Coded Aperture Snapshot Spectroscopic Imaging System (CASSI) has attracted more and more attention. However, in the case of undersampling, the effect of the reconstructed image is often unsatisfactory. For spectral imaging system, the slower the imaging speed, the higher the image resolution of hyperspectral images, and vice versa. To solve this problem, in this paper, we design an undersampling rapid coded aperture spectrometer system. Based on the analysis of the internal relationship between coded spectral imaging and single pixel imaging, the order of the discrete cosine transform (DCT) coding patterns is selected by using the energy concentration property of the DCT and the rich spatial information contained in the RGB images. In addition, we built a spectral image enhancement network to improve the quality of the reconstructed images. At the sampling rate of 0.0625, the PSNR, SSIM and SAM of the spectral images reconstructed based on our system are 43.8341, 0.9869 and 2.0862, respectively, the reconstruction effect of this method is significantly superior to other methods, and this method provides an idea to address the contradiction between image resolution and imaging speed to a certain extent, so as to achieve high speed and high resolution hyperspectral imaging. [ABSTRACT FROM AUTHOR]