Tensor-based fusion that couples the high spatial resolution of a multispectral image (MSI) to the high spectral resolution of a hyperspectral image (HSI) is considered. The fusion problem is first formulated mathematically as a convex optimization of a tensor trace norm imposing low-rank spatially as well as spectrally, with an alternating-directions optimization featuring linearization providing the solution. Although prior tensor-based fusion approaches typically resort to tensor decomposition, the proposed algorithm exploits ideas from the field of tensor completion to directly impose a low-rank property spatially and spectrally while avoiding the computationally complex patch clustering and dictionary learning common to competing fusion techniques. Additionally, small modifications to the basic optimization permit a fusion process robust to missing hyperspectral values such as those that can result from dead stripes in real hyperspectral sensors. The experimental evaluations on both synthetic imagery as well as real imagery demonstrate that the resulting low-rank tensor-approximation (LRTA) fusion algorithm preserves both spatial details and texture, yielding significantly improved image quality when compared to other state-of-the-art fusion methods as well as effective restoration under conditions of missing stripes within the HSI.

Pushbroom acquisition of hyperspectral imagery is prone to striping artifacts in the along-track direction. A hyperspectral destriping algorithm is proposed such that the subbands of a 3-D wavelet transform most affected by pushbroom stripes--namely, those with spatially vertical orientation--are the exclusive focus of destriping. The proposed method features an iterative image decomposition composed of a low-rank model for the stripes coupled with a group-sparse prior on the wavelet coefficients of the subbands in question. While low-rank stripe models have been widely used in the past, they typically have been deployed in conjunction with a total-variation prior on the image that is prone to oversmoothing and residual stripe artifacts. On the other hand, the proposed group-sparse prior not only captures the well-known sparse nature of wavelet coefficients but also capitalizes on their vertical clustering in the subbands in question. In addition, while many prior destriping methods are wavelet-based, they employ 2-D transforms band by band. In contrast, the proposed 3-D wavelet transform provides a greater concentration of stripe information into fewer wavelet coefficients, leading to more effective destriping. Experimental results on both synthetically striped imagery as well as real striped imagery from an actual hyperspectral sensor demonstrate superior image quality for the proposed method as compared with other state-of-the-art methods.

Random Gaussian noise and striping artifacts are common phenomena in hyperspectral images (HSI). In this article, an effective restoration method is proposed to simultaneously remove Gaussian noise and stripes by merging a denoising and a destriping submodel. A denoising submodel performs a multiband denoising, i.e., Gaussian noise removal, considering Gaussian noise variations between different bands, to restore the striped HSI from the corrupted image, in which the striped HSI is constrained by a weighted nuclear norm. For the destriping submodel, we propose an adaptive anisotropy total variation method to adaptively smoothen the striped HSI, and we apply, for the first time, the truncated nuclear norm to constrain the rank of the stripes to 1. After merging the above two submodels, an ultimate image restoration model is obtained for both denoising and destriping. To solve the obtained optimization problem, the alternating direction method of multipliers (ADMM) is carefully schemed to perform an alternative and mutually constrained execution of denoising and destriping. Experiments on both synthetic and real data demonstrate the effectiveness and superiority of the proposed approach.

Recently, deep learning has become one of the most popular tools for pansharpening, many relevant methods have been investigated and reflected great performance. However, a non-negligible problem is the absence of ground-truth (GT). A common solution is using degraded images as training input and the original images are employed as GT. The learned mapping between low resolution (LR) and high resolution (HR) is simulated, is not real, which may cause spectral distortion or insufficient spatial texture enhancement of fused images. In order to address the drawback, a novel unsupervised attention pansharpening net (UAP-Net) is proposed. The proposed UAP-Net mainly contains two major components: 1) the deep residual network (DRN) and 2) spatial texture attention block (STAB). The DRN aims to extract spectral features and spatial details features from low-resolution multi-spectral (LRMS) and panchromatic (PAN), and to fuse those features to make them more representative. The designed STAB adopts the high-frequency component of corresponding input PAN as the weight to enhance the spatial details of the residual block output features. Moreover, a new loss function including two spatial losses and two spectral losses are established. The losses are calculated in the spatial domain and the frequency domain, respectively. Experiments on Gaofen-2 and Worldview-2 remote sensing data demonstrate that the proposed UAP-Net could fuse PAN and LRMS images effectively without the help of high-resolution multi-spectral (HRMS). The proposed framework is fully general and can be used for many multisource remote sensing image fusion, and achieves optimal performance in terms of both the subjective visual effect and the quantitative evaluation.

Low-rank-tensor-approximation(LRTA)-based hyperspectral image and hyperspectral imagery (HSI) restoration has drawn increasing attention. However, most of the methods construct a hidden low-rank tensor by utilizing the nonlocal self-similarity (NLSS) and global spectral correlation (GSC) inherited by HSIs. Although achieving state-of-the-art (SOTA) restoration performance, NLSS and GSC have limitations. NLSS is introduced from natural image denoising to remove spatially independent identically distributed (i.i.d.) Gaussian and impulse noise, while GSC, which is naturally possessed by HSIs, is adopted to maintain the spectral integrity and remove spectrally, i.i.d., degradations. Therefore, NLSS and GSC may not be successfully used for complex HSI restoration tasks, such as destriping, cloud removal, and recovery of atmospheric absorption bands. To solve the issue, borrowing the idea from manifold learning, the geometry information characterized by proximity relationship, is integrated with the LRTA to solve the above issue, named multigraph-based LRTA (MGLRTA). Different from most of the existing methods, the proposed MGLRTA directly models an HSI as a low-rank tensor and efficiently explores the extra proximity information on the defined graphs that are not only inherited by the low-rank constraints but also naturally possessed in HSIs. A well-posed iterative algorithm is designed to solve the restoration problem. Experimental results on different datasets that cover several severe degradation scenarios demonstrate that the proposed MGLRTA outperforms the SOTA HSI restoration methods.

Recently, convolutional neural networks (CNNs) have been developed for remote sensing image fusion (RSIF). To obtain competitive fusion performance, network design becomes more complicated by stacking convolutional layers deeper and wider. However, problems still remain when applying existing networks in practical applications. On the one hand, researchers focus on improving spatial resolution but ignore that the fused images will be used in subsequent interpretation applications, e.g., objection detection. On the other hand, RSIF involves different tasks with different image sources e.g., pansharpening of the panchromatic and multispectral image, hypersharpening of the panchromatic and hyperspectral image, etc. However, existing networks only solve one of them, failing to be compatible with other tasks. To address the above problems, a convenient task-inspired multiscale nonlocal-attention network (MNAN) is proposed for RSIF. The proposed MNAN focuses more on enhancing the multi-scale targets in the scene when improving the resolution of the fused image. In addition, the proposed network can be applied to both pansharpening and hypersharpening tasks without any modification.