High Spatial Resolution Hyperspectral Image Research Articles

Multi-Resolution Collaborative Fusion of SAR, Multispectral and Hyperspectral Images for Coastal Wetlands Mapping

The hyperspectral, multispectral, and synthetic aperture radar (SAR) remote sensing images provide complementary advantages in high spectral resolution, high spatial resolution, and geometric and polarimetric properties, generally. How to effectively integrate cross-modal information to obtain a high spatial resolution hyperspectral image with the characteristics of the SAR is promising. However, due to divergent imaging mechanisms of modalities, existing SAR and optical image fusion techniques generally remain limited due to the spectral or spatial distortions, especially for complex surface features such as coastal wetlands. This paper provides, for the first time, an efficient multi-resolution collaborative fusion method for multispectral, hyperspectral, and SAR images. We improve generic multi-resolution analysis with spectral-spatial weighted modulation and spectral compensation to achieve minimal spectral loss. The backscattering gradients of SAR are guided to fuse, which is calculated from saliency gradients with edge preserving. The experiments were performed on ZiYuan-1 02D (ZY-1 02D) and GaoFen-5B (AHSI) hyperspectral, Sentinel-2 and GaoFen-5B (VIMI) multispectral, and Sentinel-1 SAR images in the challenging coastal wetlands. Specifically, the fusion results were comprehensively tested and verified on the qualitative, quantitative, and classification metrics. The experimental results show the competitive performance of the proposed method.

Robust Hyperspectral Image Fusion With Simultaneous Guide Image Denoising via Constrained Convex Optimization

The paper proposes a new high spatial resolution hyperspectral (HR-HS) image estimation method based on convex optimization. The method assumes a low spatial resolution HS (LR-HS) image and a guide image as observations, where both observations are contaminated by noise. Our method simultaneously estimates an HR-HS image and a noiseless guide image, so the method can utilize spatial information in a guide image even if it is contaminated by heavy noise. The proposed estimation problem adopts hybrid spatio-spectral total variation as regularization and evaluates the edge similarity between HR-HS and guide images to effectively use apriori knowledge on an HR-HS image and spatial detail information in a guide image. To efficiently solve the problem, we apply a primal-dual splitting method. Experiments demonstrate the performance of our method and the advantage over several existing methods.

Deep Multiscale Feedback Network for Hyperspectral Image Fusion

Hyperspectral imaging is useful in many remote sensing tasks. However, it is often challenging to obtain high-resolution images in both the spatial and spectral domains due to hardware limitations. Hyperspectral image fusion (HIF) solves this problem by fusing a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral images (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI). Many methods have been proposed for HIF, but few approaches have explored the multiscale mutual dependencies between LR-HSI, HR-MSI, and HR-HSI. This kind of mutual dependencies come from the fact that LR-HSI, HR-MSI, and HR-HSI capture the same scene with different spatial or spectral resolutions. To this end, we propose a deep multiscale feedback network (DMFBN) that iteratively learns image fusion and degeneration for HIF. We further equip the network with an error feedback mechanism coupled with multiscale feature learning. Both strategies help better learn the mutual dependencies. Extensive quantitative and qualitative evaluations on two public datasets show that the proposed method performs favorably against the state-of-the-art (SOTA) methods.

Deep Unfolding Network for Spatiospectral Image Super-Resolution

In this paper, we explore the spatiospectral image super-resolution (SSSR) task, <i>i.e.</i>, joint spatial and spectral super-resolution, which aims to generate a high spatial resolution hyperspectral image (HR-HSI) from a low spatial resolution multispectral image (LR-MSI). To tackle such a severely ill-posed problem, one straightforward but inefficient way is to sequentially perform a single image super-resolution (SISR) network followed by a spectral super-resolution (SSR) network in a two-stage manner or reverse order. In this paper, we propose a model-based deep learning network for SSSR task, named unfolding spatiospectral super-resolution network (US3RN), which not only uses closed-form solutions to solve SISR subproblem and SSR subproblem, but also has extremely small parameters (only 295 K). In specific, we reformulate the image degradation and incorporate the spatiospectral super-resolution (SSSR) model, which takes the observation models of SISR and SSR into consideration. Then we solve the model-based energy function via the alternative direction multiplier method (ADMM) technique. Finally, we unfold the iterative process of the ADMM algorithm into a multistage network. Therefore, US3RN combines the merits of interpretability and generality of model-based methods with the advantages of learning-based methods. The experimental results show that, compared with the two-step method, US3RN achieves better results both quantitatively and qualitatively, while sharply reducing the number of parameters and FLOPs. Source code will be available at <uri>https://github.com/junjun-jiang/US3RN</uri>.

An Iterative Regularization Method Based on Tensor Subspace Representation for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But, how to precisely reconstruct the high spatial resolution hyperspectral image (HR-HSI) by fusion technology is a challenging issue. In this paper, we propose an iterative regularization method based on tensor subspace representation (IR-TenSR) for MSI-HSI fusion, thus HSI-SR. First, we propose a tensor subspace representation (TenSR)-based regularization model that integrates the global spectral-spatial low-rank and the nonlocal self-similarity priors of HR-HSI. These two priors have been proven effective, but previous HSI-SR works cannot simultaneously exploit them. Subsequently, we design an iterative regularization procedure to utilize the residual information of acquired low-resolution images, which are ignored in other works that produce suboptimal results. Finally, we develop an effective algorithm based on the proximal alternating minimization method to solve the TenSR-regularization model. With that, we obtain the iterative regularization algorithm. Experiments implemented on the simulated and real datasets illustrate the advantages of the proposed IR-TenSR compared with state-of-the-art fusion approaches. The code is available at https://github.com/liangjiandeng/IR-TenSR.

Integrated Fusion for Panchromatic, Multispectral, Hyperspectral Remote Sensing Images With Different Swath Widths

Zi Yuan (ZY)-1 02D satellite simultaneously provides the low spatial resolution (LR) and narrow swath-width hyperspectral (HS) image, the moderate spatial resolution (MR) multispectral (MS) image with a wider swath width, and the high spatial resolution (HR) panchromatic (PAN) image with the same wide swath width to the MR MS. How to comprehensively integrate their complementary advantages to obtain the wide swath-width and high-fidelity HR HS image is interesting but challenging. In this paper, we propose an integrated fusion method for the HR PAN, MR MS, and LR HS images with different swath widths, to generate the optimal wide swath-width HR HS image. The proposed method is based on the encoder-decoder learning framework. In the proposed fusion framework, a novel multi-branch encoder structure with an enhanced HS-encoder module and the multilevel spatial-spectral aggregation block is designed, by considering the difference in the spatial and spectral resolution among the multi-sensor images. The experiments on synthetic and real datasets from both qualitative and quantitative aspects demonstrated the competitive performance of the proposed method.

Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on handcrafted priors. Inspired by the specific properties of model, we make the first attempt to design a model-inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs, respectively. In the encoding stage, we present a pixelwise fusion model to estimate hidden outputs directly and then reformulate and unfold the model’s algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model and can be trained patch by patch rather than the entire images. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.

Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

Fusion of Hyperspectral-Multispectral images joining Spatial-Spectral Dual-Dictionary and structured sparse Low-rank representation

High spatial resolution hyperspectral images (HR-HSIs) have shown considerable potential in urban green infrastructure monitoring. A prevalent scheme to overcome spatial resolution limitations in HSIs is by fusing low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs). Existing methods considering the spectral dictionary or spatial dictionary can only reflect the unilateral characteristics of the HSI and cannot completely restore full information in the latent HSI. To overcome this issue, we propose a novel HSI-MSI fusion method, named DDSSLR, which joins spatial-spectral dual-dictionary and structured sparse low-rank representation. The spectral dictionary characterizing generalized spectra and the corresponding spectral sparse coefficients are extracted from LR-HSI and HR-MSI, while sparse low-rank priors of the local structure are imposed on the spectral pixels within the same superpixel in HR-MSI. Additionally, in the spatial domain, we exploit the remaining high-frequency components to learn the spatial dictionary and use the unitary transformation to factorize the spatial sparse coefficient into the sparse low-rank matrix in subspace, establishing the relationship between low-rank and sparse. We formulate the two fusion models as variational optimization problems, which are effectively solved by the alternating direction methods of multipliers (ADMM). Experiments on three HSI datasets show that DDSSLR achieves state-of-the-art performance.

A Hardware Accelerator for Onboard Spatial Resolution Enhancement of Hyperspectral Images

Hyperspectral images (HSIs) are the images that have a high spectral resolution with hundreds of bands. HSIs are widely used in the precise identification and classification of the materials and surfaces. However, they have a low spatial resolution caused by the limited capacity of the sensors responsible for capturing the images. In view of this, the preprocessing techniques are used to increase the spatial resolution of the HSIs and enable greater precision in the subsequent processing stages. This letter presents the development of a hardware accelerator specially designed to process a pansharpening algorithm that fuses the hyperspectral and panchromatic images to produce a high spatial resolution HSI. The experimental results demonstrate that the processor is energy-efficient and can be used for onboard processing in a small spacecraft, such as the satellites.

Deep Blind Hyperspectral Image Super-Resolution.

The production of a high spatial resolution (HR) hyperspectral image (HSI) through the fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has underpinned much of the recent progress in HSI super-resolution. The premise of these signs of progress is that both the degeneration from the HR HSI to LR HSI in the spatial domain and the degeneration from the HR HSI to HR MSI in the spectral domain are assumed to be known in advance. However, such a premise is difficult to achieve in practice. To address this problem, we propose to incorporate degeneration estimation into HSI super-resolution and present an unsupervised deep framework for "blind" HSIs super-resolution where the degenerations in both domains are unknown. In this framework, we model the latent HR HSI and the unknown degenerations with deep network structures to regularize them instead of using handcrafted (or shallow) priors. Specifically, we generate the latent HR HSI with an image-specific generator network and structure the degenerations in spatial and spectral domains through a convolution layer and a fully connected layer, respectively. By doing this, the proposed framework can be formulated as an end-to-end deep network learning problem, which is purely supervised by those two input images (i.e., LR HSI and HR MSI) and can be effectively solved by the backpropagation algorithm. Experiments on both natural scene and remote sensing HSI data sets show the superior performance of the proposed method in coping with unknown degeneration either in the spatial domain, spectral domain, or even both of them.

Progressive Spatial–Spectral Joint Network for Hyperspectral Image Reconstruction

Hyperspectral (HS) images are widely used to identify and characterize objects in a scene of interest with high acquisition costs and low spatial resolution. It is an inexpensive way to obtain high-spatial-resolution HS images (HSIs) by spectral reconstruction from high-spatial-resolution multispectral (MS) images. In this article, we proposed a progressive spatial&#x2013;spectral joint network (PSJN) to reconstruct HSIs from MS images. PSJN is composed of a 2-D spatial feature extraction module, a 3-D progressive spatial&#x2013;spectral feature construction module, and a spectral postprocessing module. PSJN makes full use of the shallow spatial features extracted by the 2-D spatial feature extraction module with the spatial&#x2013;spectral features extracted by the 3-D progressive spatial&#x2013;spectral feature construction module. The 3-D progressive spatial&#x2013;spectral feature construction module is designed to extract spatial&#x2013;spectral information from local spectra in local space and construct spectral information from a few bands to a lot of bands in a pyramidal structure. Besides, a network updating mechanism is proposed to improve the spectral reconstruction effect of the images with poor original spectral reconstruction effect. The experimental results on three HS&#x2013;MS datasets and one MS dataset demonstrate the efficacy of the proposed methods. Compared with the most advanced spectral reconstruction methods based on dictionary learning and deep learning, our method achieves the best performance of the latest methods in similarity evaluation and classification performance evaluation.

Correction of Sunglint Effects in High Spatial Resolution Hyperspectral Imagery Using SWIR or NIR Bands and Taking Account of Spectral Variation of Refractive Index of Water

Passive high spatial resolution hyperspectral and multispectral imaging systems in the solar spectral region from aircraft and satellite platforms are being increasingly used for remote sensing of coastal waters and inland lakes. However, the remotely sensed data are often contaminated by the specular reflection of solar radiation at the air/water interface. Thus, the effects of sunglint need to be corrected. The purpose of sunglint correction is to remove the undesired specular reflected portion of radiance that does not carry any information about the water body and its bottom surface properties and to obtain the water-leaving radiance from the measured total radiance. In general, adequate treatment of the spectral variation of the sunglint effect with due consideration of the physics and the spectroscopy principles has not been done in previously reported hyperspectral sunglint removing techniques. In this study, a practical sunglint removing technique using either a shortwave infrared (SWIR) band or a near-infrared (NIR) band has been developed, where the liquid water absorption is sufficiently large, and the water-leaving radiance (in units of reflectance) is negligible. In the proposed method, proper consideration has been taken for the spectral Fresnel reflection in the wavelength range of 0.35–2.5 mm. Four cases of sunglint removal from hyperspectral imaging data acquired from two airborne imaging spectrometers have been presented.

Hyperspectral image super-resolution via subspace-based fast low tensor multi-rank regularization

The demand for acquiring a high-spatial-resolution hyperspectral image (HR-HSI) imposes challenges to existing hyperspectral imaging tasks. Generally, fusing a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI) is likely the most viable path towards HR-HSI. The Low Tensor Multi-Rank (LTMR) method is demonstrated a good fusion performance. However, it takes a long execution time. To rectify this problem, we propose a fast low tensor multi-rank (FLTMR) hyperspectral super-resolution method to improve the performance in three aspects. To speed up the estimation of spectral coefficients, a set of characteristic values representing HR-HSI are extracted from LR-HSI as the truncation value of tensor reconstruction. To further optimize the calculation time of the algorithm, a truncated value weight coefficient is introduced. Furthermore, applying the iterative attenuation coefficient to the iterative solution can accelerate and achieve better coefficient estimation results. Consequently, our FLTMR approach alleviates the excellent fusion results’ requirements and enables rapid processing of the fusion. It can be illustrated that our algorithm consumes only 1/4 to 2/3 of the time while achieves better or similar performance to LTMR. Essentially, owing to our proposed method’s excellent performance in the coefficient tensor reconstruction, our method may have important significance for the study of tensor reconstruction.

SSR-NET: Spatial–Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion

The fusion of a low-spatial-resolution hyperspectral image (HSI) (LR-HSI) with its corresponding high-spatial-resolution multispectral image (MSI) (HR-MSI) to reconstruct a high-spatial-resolution HSI (HR-HSI) has been a significant subject in recent years. Nevertheless, it is still difficult to achieve the cross-mode information fusion of spatial mode and spectral mode when reconstructing HR-HSI for the existing methods. In this article, based on a convolutional neural network (CNN), an interpretable spatial-spectral reconstruction network (SSR-NET) is proposed for more efficient HSI and MSI fusion. More specifically, the proposed SSR-NET is a physical straightforward model that consists of three components: 1) cross-mode message inserting (CMMI); this operation can produce the preliminary fused HR-HSI, preserving the most valuable information of LR-HSI and HR-MSI; 2) spatial reconstruction network (SpatRN); the SpatRN concentrates on reconstructing the lost spatial information of LR-HSI with the guidance of spatial edge loss ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> spat</sub> ); and 3) spectral reconstruction network (SpecRN); the SpecRN pays attention to reconstruct the lost spectral information of HR-MSI under the constraint of spatial edge loss ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> spec</sub> ). Comparative experiments are conducted on six HSI data sets of Urban, Pavia University (PU), Pavia Center (PC), Botswana, Indian Pines (IP), and Washington DC Mall (WDCM), and the proposed SSR-NET achieves the superior or competitive results in comparison with seven state-of-the-art methods. The code of SSR-NET is available at https://github.com/hw2hwei/SSRNET.

Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network

Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial–spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial–spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications.

Joint sparse-collaborative representation to fuse hyperspectral and multispectral images

Representation based methods to fuse a low spatial resolution hyperspectral image (LS-HSI) and a high spatial resolution multispectral image (HS-MSI) for reconstructing a high spatial resolution hyperspectral image (HS-HSI) have attracted increasing interest in recent years. Existing representation based algorithms only emphasize the sparsity of data, ignoring the collaboration, which may cause fusion performance degradation. In this paper, we develop a novel fusion method based on joint sparse-collaborative representation (SCR) for LS-HSI and HS-MSI. The SCR method consists of three steps: 1) sparse and collaborative dictionaries are learned to extract the spectral information of the given LS-HSI from two perspectives; 2) the turbopixel based segmentation is used for obtaining unfixed-size patches to describe the complex local structure of the HS-MSI; 3) the joint sparse-collaborative representation model is established for patch representing to reconstruct the HS-HSI. Compared with existing representation based strategies, the SCR not only considers the data sparsity, but also preserves the collaboration reflecting correlations among different spectral bands. In addition, the CSR more sufficiently utilizes the context of the given data, relying on unfixed-size patch dividing with adaptive adjustment by the turbopixel based segmentation. Experimental results indicate that the SCR achieves better performance than several state-of-the-art algorithms.

Hyperspectral image super-resolution combining with deep learning and spectral unmixing

In recent years, hyperspectral image super-resolution has attracted the attention of many researchers and has become a hot topic in the field of computer vision. However, it is difficult to obtain high-resolution images due to imaging hardware devices. At present, many existing hyperspectral image super-resolution methods have not achieved good results. In this paper, we propose a hyperspectral image super-resolution method combining with deep residual convolutional neural network (DRCNN) and spectral unmixing. Firstly, the spatial resolution of the image is enhanced by learning a priori knowledge of natural images. The DRCNN reconstructs high spatial resolution hyperspectral images by concatenating multiple residual blocks, each containing two convolutional layers. Secondly, the spectral features of low-resolution and high-resolution hyperspectral images are linked by spectral unmixing. This approach aims to obtain the endmember matrix and the abundance matrix. The final reconstruction result is obtained by multiplying the endmember matrix and the abundance matrix. In addition, in order to improve the visual effect of the reconstructed image, the total variation regularity is used to impose constraints on the abundance matrix to enhance the relationship between the pixels. The experimental results of remote sensing data based on ground facts show that the proposed method has good performance and preserves spatial information and spectral information without the need for auxiliary images.

Hyperspectral Image Superresolution Using Unidirectional Total Variation With Tucker Decomposition

The hyperspectral image superresolution (HSI-SR) problem aims to improve the spatial quality of a low spatial resolution HSI by fusing the LR-HSI with the corresponding high spatial resolution multispectral image. The generated HSI with high spatial quality, i.e., the target high spatial resolution hyperspectral image (HR-HSI), generally has some fundamental latent properties, e.g., the sparsity, and the piecewise smoothness along with the three modes (i.e., width, height, and spectral mode). However, limited works consider both properties in the HSI-SR problem. In this work, a novel unidirectional total variation (TV) based approach is been proposed. On the one hand, we consider that the target HR-HSI exhibits both the sparsity and the piecewise smoothness on the three modes, and they can be depicted well by the $\ell _1$ -norm and TV, respectively. On the other hand, we utilize the classical Tucker decomposition to decompose the target HR-HSI (a three-mode tensor) as a sparse core tensor multiplied by the dictionary matrices along with the three modes. Especially, we impose the $\ell _1$ -norm on core tensor to characterize the sparsity and the unidirectional TV on three dictionaries to characterize the piecewise smoothness. The proximal alternating optimization scheme and the alternating direction method of multipliers are used to iteratively solve the proposed model. Experiments on three common datasets illustrate that the proposed approach has better performance than some current state-of-the-art HSI-SR methods. Please find source code from: https://liangjiandeng.github.io/ .

Spectral Diversity Successfully Estimates the α-Diversity of Biocrust-Forming Lichens

Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon’s, Simpson’s, and Pielou’s indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson’s index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands.