Hyperspectral Compressive Sensing Research Articles

Compressive sensing reconstruction of hyperspectral images based on codec space‐spectrum joint dense residual network

The spatial and spectral information contained in the hyperspectral image (HSI) make it widely used in many fields. However, the sharp increase of HSI data brings enormous pressure to the data storage and real-time transmission. The research shows that hyperspectral compressive sensing (HCS) breaks through the bottleneck of the Nyquist sampling theorem, which can relieve the massive pressure on data storage and real-time transmission. Existing HCS methods try to design advanced compression sampling matrix or reconstruction algorithms, but cannot connect the two through a unified framework. To further improve the image reconstruction quality, a novel codec space-spectrum joint dense residual network (CDS2-DResN) is proposed. The CDS2-DResN is divided into block compression sampling part and reconstruction part. For block compression sampling, coded convolutional layer (CCL) is leveraged to compress and sample HSI. For measurements reconstruction, deconvolution layer is first leveraged to initially reconstruct HSI, and then build a space-spectrum joint network to refine the initial reconstructed HSI. Moreover, the CCL and reconstruction network are optimized via a unified framework, which can simplify the pre-processing and post-processing process of HCS. Extensive experiments have shown that CDS2-DResN has an excellent HCS reconstruction effect at measurement rates 0.25, 0.10, 0.04 and 0.01, respectively.

Exploring error of linear mixed model for hyperspectral image reconstruction from spectral compressive sensing

Linear mixed model (LMM) has been extensively applied for hyperspectral compressive sensing (CS) in recent years. However, the error introduced by LMM that limits the reconstruction performance has not been given full consideration. We propose an algorithm for hyperspectral CS based on LMM under the assumption of known endmembers. At the sampling stage, only spectral compressive sampling is carried out to keep the abundance information as much as possible. At the reconstruction stage, the proposed algorithm estimates abundance by using linear unmixing from the spectral observed data. Moreover, the model error introduced by LMM is explored; a joint convex optimization scheme for estimation of both abundance and model error is established and solved by the alternating iteration approach to achieve the optimal reconstruction. Experimental results on a real hyperspectral dataset demonstrate that the proposed algorithm significantly outperforms the other state-of-the-art hyperspectral CS algorithms.

Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing

The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models. With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved.

Exploring Structured Sparsity by a Reweighted Laplace Prior for Hyperspectral Compressive Sensing

Hyperspectral compressive sensing (HCS) can greatly reduce the enormous cost of hyperspectral images (HSIs) on imaging, storage, and transmission by only collecting a few compressive measurements in the image acquisition. One of the most challenging problems for HCS is how to reconstruct the HSI accurately from such a few measurements. It has been proved that introducing structure information into sparsity prior can improve the reconstruction performance of standard compressive sensing models. However, the structured sparsity of HSIs is unknown in reality and easily affected by random noise, which makes it difficult to explore the structured sparsity in HCS. To address this problem, we propose a novel reweighted Laplace prior-based HCS method in this paper. First, a hierarchical reweighted Laplace prior is proposed to model the distribution of sparsity in an HSI, which relieves the undemocratic penalization of traditional Laplace prior on nonzero coefficients of a sparse signal. Then, a latent variable-based Bayesian model is employed to learn the optimal configuration of the reweighted Laplace prior from the measurements. This model unifies signal recovery, sparsity prior learning, and noise estimation into a variational framework, where these three tasks are alternatively optimized till convergence. The finally learned sparsity prior can well represent the underlying structure in the sparse signal and is adaptive to the unknown noise. These advantages together improve the reconstruction accuracy of HCS obviously. Moreover, the proposed method is extended to learn a matrix normal distribution-based prior with a full covariance matrix, which depicts the underlying structure in the sparse signal better. As a result, the reconstruction accuracy is further improved. Extensive experimental results on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy.

Locally Similar Sparsity-Based Hyperspectral Compressive Sensing Using Unmixing

Linear unmixing-based compressive sensing has been extensively exploited for hyperspectral image (HSI) compression in recent years among which gradient sparsity is widely used to characterize the spatial continuity of abundance matrix given a small amount of endmembers. Though these methods have achieved good reconstruction results, identifying necessary endmembers from an HSI is challenging for them. In this study, instead of using a small amount of given endmembers, a locally similar sparsity-based hyperspectral unmixing compressive sensing (LSSHUCS) method is proposed to unmix the HSI with an established redundant endmember library. Considering that each pixel is a mixture of several endmembers, a novel locally similar sparsity constraint is imposed on the abundance matrix, which depicts the sparsity of abundance vectors and the local similarity among those sparse vectors simultaneously. This constraint guarantees to reconstruct the HSI precisely even with a quite low sample rate and can select the necessary endmembers from the endmember library automatically for unmixing. LSSHUCS is further extended to a more general one, which tactfully settles the spectrum variation problem, and an augmented Lagrangian algorithm is elaborated meticulously to solve the inverse linear problem in LSSHUCS. Extensive experimental results on both synthetic and real hyperspectral data demonstrate that the proposed method surpasses several state-of-the-art methods on reconstruction accuracy.