Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images. In recent years, research on RGB SR has shown that models pre-trained on large-scale benchmark datasets can greatly improve performance on unseen data, which may stand as a remedy for HSI. But how can we transfer the pre-trained RGB model to HSI, to overcome the data-scarcity bottleneck? Because of the significant difference in the channels between the pre-trained RGB model and the HSI, the model cannot focus on the correlation along the spectral dimension, thus limiting its ability to utilize on HSI. Inspired by the HSI spatial-spectral decoupling, we propose a new framework that first fine-tunes the pre-trained model with the spatial components (known as eigenimages), and then infers on unseen HSI using an iterative spectral regularization (ISR) to maintain the spectral correlation. The advantages of our method lie in: 1) we effectively inject the spatial texture processing capabilities of the pre-trained RGB model into HSI while keeping spectral fidelity, 2) learning in the spectral-decorrelated domain can improve the generalizability to spectral-agnostic data, and 3) our inference in the eigenimage domain naturally exploits the spectral low-rank property of HSI, thereby reducing the complexity. This work bridges the gap between pre-trained RGB models and HSI via eigenimages, addressing the issue of limited HSI training data, hence the name EigenSR. Extensive experiments show that EigenSR outperforms the state-of-the-art (SOTA) methods in both spatial and spectral metrics.

Current research in hyperspectral anomaly detection often incorporates low-rank (LR) or total variation (TV) priors to encode the background matrix. However, applying such regularizers to the detection model increases the computational burden. In this letter, we propose a subspace transformation learning-based anomaly detector (termed STLAD). In STLAD, we employ an orthogonal transformation to represent the background in its subspace, where both the background and the transformation share spatial smoothness prior and approximate sparsity properties based on carefully selected basis vectors. By leveraging this background characterization, the anomaly component can be effectively described using the ℓ2,1 mixed norm. To solve the STLAD model, we design an alternating direction method of multipliers (ADMM) with guaranteed convergence. Experiments conducted on benchmark hyperspectral datasets demonstrate that STLAD outperforms several state-of-the-art anomaly detection methods. The demo of STLAD will be publicly available at: https://github.com/XiangfeiShen/STLAD.

Hyperspectral anomaly detection (HAD) has attracted increasing attention due to its economical and efficient applications. The main challenge lies in the data-starved problem of hyperspectral images (HSIs) and the costliness of manual annotation, making it heavily reliant on the model’s adaptability and robustness to unseen scenes under limited samples. Self-supervised learning offers a solution to this urgency via mining meaningful representations from the data itself. One promising paradigm is leveraging untrained neural networks to reconstruct the background component for revealing anomalous information. Its capability stems from the network architecture and the training process rather than learning from expensive and strongly domain-dependent data, which is naturally applicable to HAD. In this article, to handle the urgent requirement for self-supervised learning in HAD, we propose a multiscale network (termed MSNet) that detects anomalies with enhanced separation training. The network architecture consists of several multiscale convolutional encoder-decoder (CED) layers, considering the spatial characteristics of the anomalies. To suppress the anomalies during background reconstruction, we adopt a new separation training strategy by introducing a soft separator for better practicality on larger datasets. Extensive experiments conducted on five commonly used datasets and the HAD100 dataset, demonstrate the superiority of our method over its counterparts. Our code is available athttps://github.com/enter-i-username/MSNet.

Now it is still a challenge to compress high-throughput hyperspectral tensor image data on lightweight air-carried/spaceborne remote sensing systems, primarily due to insufficient computational resources and limited transmission bandwidth. To address this challenge, we propose a bit-level tensor data compression network (BTC-Net) that provides higher compression performance by leveraging a data-driven lightweight quantized neural encoder with two-stage bit compression. The BTC-Net achieves semantic near-lossless high reconstruction quality at low compression bit rates thanks to its optimized decoder, which uses a channel-wise attention-based enhancement module to recover hyperspectral tensor data. Experimental results on different hyperspectral datasets show that the BTC-Net could achieve an extremely low compression bit rate of fewer than 0.04 bits per pixel per band (bpppb) with state-of-the-art reconstruction performances. The demo of BTC-Net will be publicly available online at: https://github.com/zx20173646/BTCNet.

The sparse regression method is known for its ability to unmix hyperspectral data, but it can be computationally expensive and accurately insufficient due to the large scale and high coherence of the spectral library. To address this issue, a new approach called layered sparse regression unmixing (termed LSU) has been proposed in this paper. This method involves breaking down the sparse unmixing process into multilayers, each of which interactively learns a row-sparsity-promoting abundance matrix and fine-tunes active library atoms based on measured activeness. By doing so, LSU outputs both a learned abundance matrix and an optimal library that can best model each mixed pixel in the scene. The proposed LSU can be efficiently solved by the alternating direction method of the multipliers framework. Experimental results obtained from simulated and real hyperspectral images demonstrate the effectiveness of LSU. The demo of the proposed LSU will be publicly available at https://github.com/XiangfeiShen/Layered_Sparse_Regression_Unmixing.

Hyperspectral anomaly detection aims to separate sparse anomalies from low-rank background components. A variety of detectors have been proposed to identify anomalies, but most of them tend to emphasize characterizing backgrounds with multiple types of prior knowledge and limited information on anomaly components. To tackle these issues, this article simultaneously focuses on two components and proposes a matrix factorization method with framelet and saliency priors to handle the anomaly detection problem. We first employ a framelet to characterize nonnegative background representation coefficients, as they can jointly maintain sparsity and piecewise smoothness after framelet decomposition. We then exploit saliency prior knowledge to measure each pixel’s potential to be an anomaly. Finally, we incorporate the pure pixel index (PPI) with Reed-Xiaoli’s (RX) method to possess representative dictionary atoms. We solve the optimization problem using a block successive upper-bound minimization (BSUM) framework with guaranteed convergence. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed method outperforms some state-of-the-art anomaly detection methods.

Transformer networks have shown impressive performance for hyperspectral interpretation. Nevertheless, the high-dimensional redundant spectral distribution of hyperspectral images (HSIs) hinders their validity of interaction between features from distant locations. In this letter, we propose the HSI-Mixer, a novel extremely simple convolution neural network (CNN), which is similar in spirit to Transformer to re-consider the remarkable inductive biases of convolutions. In specific, we construct a hybrid measurement-based linear projection (HMLP) to merge spectral signatures and spatial positions of an HSI cuboid. Meanwhile, according to the merging relations between spectral-spatial attributes, we establish both spectral and spatial Mixer blocks to separate features from a mixed volume to a pure one, across either spectral bands or spatial locations, respectively. Furthermore, our HSI-Mixer maintains the same-depth-and-resolution throughout the network. Experimental results on three benchmark datasets demonstrate that our proposal achieves promising performance, in contrast to other state-of-the-art methods. The codes of this work will be available at https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer.

This letter proposes a two-phase strategy-based grouped collaborative representation classifier (CRC) for hyperspectral image classification. Specifically, a spectral correlation-based CRC (SCCR) is proposed in the first phase, which considers a regularization term that uses the spectral correlations between the test sample and training samples. The representation coefficient vector generated by SCCR is then transformed into class-specific group weights. In the second phase, we integrate group weights and spectral correlations into the CRC and propose a grouped CRC (GCRC). Experimental results obtained from three real hyperspectral data sets demonstrate that the proposed SCCR and GCRC can provide better classification performance over other state-of-the-art representation-based classifiers.

Sparse regression relaxes the difficulties of blind unmixing of hyperspectral data thanks to the spectral library. Many investigations, however, attach importance to global priors such as sparsity and low-rankness. This letter proposes a local-global-based sparse regression unmixing method, called LGSU, by introducing a local sparsity regularization to help boost the unmixing performance that only considers global sparsity. The proposed LGSU first uses a superpixel-based technique to yield a set of homogeneous superpixels for guiding local sparse regularization purposes. LGSU then considers a traditional ℓ1regularization to enhance global sparsity. Coupling with local and global sparsity constraints, the proposed LGSU can effectively estimate the abundance of a given image via the alternating direction method of multipliers. Experimental results obtained from synthetic and real hyperspectral images demonstrate the effectiveness of the proposed algorithm.

Many unmixing methods hold the assumption that endmembers correspond to major land-covers, but not true for some unmixing tasks where observed minor object signals corresponding to some special types of endmembers are relatively weak. When there exist weak signals that have low intensity potentially caused by subtle mixing abundance fractions regarding the endmembers of minor objects, the traditional unmixing techniques may fail. This paper pioneers weak signal scenarios in hyperspectral unmixing using an efficient method called HyperWeak. Specifically, HyperWeak involves a sparse nonnegative matrix factorization model that contains two main parts, where the unsupervised part estimates the endmember and abundance matrices, and the supervised part ensures the minimal degradation of prior knowledge. To enhance the robustness of the HyperWeak model, this paper considers a reweighted sparsity constraint to boost the sparseness of the abundance matrix. For effectively solving optimization problems, Nesterov’s optimal gradient method is used in this paper. Experiments conducted on synthetic and real hyperspectral images indicate that HyperWeak can improve the unmixing performances of hyperspectral data in weak signal situations.

Nonnegative matrix factorization (NMF) has been widely used in hyperspectral unmixing (HU) in recent years since it can simultaneously estimate endmember and abundance matrices. However, most existing NMF unmixing methods only impose geometric or statistical unilateral prior on endmember or abundance matrix, meanwhile ignore the synergistic effect of both priors. To overcome this problem, in this paper, we propose a novel geometric and statistical hybrid method, called the weighted Ly2sparse total variation regularized and volume prior constrained NMF (wL1/2TVVC- NMF).The proposed approach integrates the endmember volume, abundance sparsity and piecewise smoothness into the unified NMF unmixing framework, and imposes the two kinds of prior information to the matrix factors simultaneously. It not only captures the sparsity and smoothness of abundance map, but also enhances the endmember identification accuracy, and improves the stability of results and noise robustness. The optimization model is simply solved by the variable splitting and augmented Lagrangian algorithm. Several experiments were conducted to demonstrate the performance of proposed method.