In this paper, a dual-path optimized fusion network based on spectral unmixing (DPOSU) is proposed for the fusion of hyperspectral image (HSI) and multispectral image (MSI). Based on the spectral mixing model of HSI, an endmember optimization model and an abundance optimization model are constructed respectively. Combining with the observation model, a fusion model for HSI and MSI is then derived. To address the unknown spectral and spatial degradation matrices in the optimization models, a dual-path optimization network is constructed to iteratively update endmember and abundance. Comprehensive experimental results illustrate that the proposed DPOSU network outperforms several typical traditional fusion methods as well as some representative deep learning based fusion methods both visually and quantitatively.

Collaborative representation (CR) has been demonstrated to be very effective for hyperspectral image classification. However, insufficient diversity of training samples often results in limited classification accuracy under small-training-sample conditions, especially when diverse spectral variation is presented in testing samples. In order to alleviate such a problem, a spectral variation augmented-based linear mixed model (SV-LMM) is proposed, in which the spectral variation is extracted by conducting singular value decomposition (SVD) over training samples. Such spectral variation is further utilized to extend the CR for hyperspectral classification. Experiments over two benchmark datasets, i.e., the Pavia Center dataset and the University of Houston dataset, demonstrate that the proposed extended CR-based classifier (ECRC) clearly improves the performance of conventional CRC for hyperspectral classification and outperforms several state-of-the-art algorithms.

Deep learning-based methods have drawn great attention in hyperspectral unmixing and obtained promising performance due to their powerful learning capability. However, few existing networks explicitly deal with the spectral variability inevitably present in hyperspectral images, limiting their fitting performance. In this letter, a spectral variability augmented two-stream network (SVATN) is designed to explicitly address the problem of spectral variability in a deep convolutional network for sparse unmixing. Specifically, the proposed SVATN maps a random input to coefficients of spectral variability in addition to abundances of endmembers, in which spectral variability is accommodated by the linear mixture model as an augmented item. Moreover, a spatial-spectral correlation-based variability extraction method (SSCVE) is proposed to construct a spectral variability library, which serves as priors in the loss function to optimize the proposed SVATN. Experiments over synthetic and real data sets demonstrate the superiority of the proposed SVATN over several state-of-the-art methods. The code of our proposed method is released at: https://github.com/MeiShaohui/SVATN.

Due to variation of imaging conditions, spectra of the same type of ground objects usually exhibit certain discrepancy, leading to intra-class spectral distance increase and inter-class distance decrease. As a result, classification accuracy is greatly affected, especially in cases with few labeled samples. For representation based classifiers, the spectral variability within limited training samples is far from sufficient to represent diverse variations within testing ones. To handle this problem, a spectral variation augmented representation for hyperspectral imagery classification (SVARC) with few labeled samples is proposed in this article. Firstly, a novel class-independent and class-dependent components based linear representation model (CICD-LRM) is proposed to emphasize the representation of spectral variation. Secondly, depending on spatial and spectral correlation, the CICD-LRM guided global and local spectral variation extraction schemes are designed, and a fused spectral variation dictionary is constructed by concatenation. Finally, a classifier for hyperspectral images based on the CICD-LRM and spectral variation dictionary is proposed, and specifically three different spectral variation reconstruction strategies are designed. Similar to most of the representation based classifiers, residual-driven decision is also employed in the proposed classifier. Comparative experiments are conducted with eight classical and state-of-the-art methods using two benchmark datasets. The experimental results demonstrate that the proposed SVARC method significantly outperforms the compared ones in cases with few labeled samples.

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the product of endmembers and their corresponding fractional abundances, which has been widely used in hyperspectral imagery analysis. However, the endmember spectra even for pixels from the same material of an image may include variability due to the influence of lighting conditions and inherent properties of materials within different pixels. Though thein situspectral library has been used to accommodate such variability by using multiplein situspectra to represent each kind of material, the performance improvement may be restricted due to the limited number of endmembers for each material. Therefore, in this article, spectral variability is directly extracted from anin situendmember library and considered to be transferable among different endmembers for the first time. Furthermore, such a spectral variability is further used to augment sparse unmixing by synchronously performing endmember-based reconstruction and spectral variability-augmented reconstruction in the sparse unmixing model. By, respectively, imposing sparse and smoothness regularization over abundances and variability coefficients, a convex optimization-based spectral variability augmented sparse unmixing (SVASU) is finally proposed, and its convergence performance is also analyzed. Experiments conducted over synthetic and real-world datasets demonstrate that the proposed SVASU method not only significantly improves the unmixing performance of conventional spectral library-based unmixing but also outperforms several state-of-the-art sparse unmixing algorithms.

Classification has been one of the most important applications of hyperspectral images (HSIs) in the past decade, because of the outstanding discrimination among different classes ensured by abundant and detailed spectral information enclosed in HSIs. While the classification accuracy must be guaranteed by plenty of training samples, which is difficult to be satisfied in many practical cases. Meanwhile, because of its comparatively low spatial resolution, mixed pixels are widely existed in HSIs which makes subpixel level classification techniques more preferable rather than traditional pixel-level ones. A novel super-resolution classification method is proposed in this paper to deal with the two above mentioned problems in HSI classification, that is, limited number of training samples and widely existed mixed pixels. Specifically, spectral variation is considered to construct spectral variation extended endmember library, with which the abundance fractions for each class within a mixed pixel are estimated using collaborative representation. And finally, the classification result with higher spatial resolution is obtained with subpixel spatial attraction model based subpixel mapping. Simulative experiments are employed for validation and comparison. Experimental results illustrate that the newly proposed method is capable of producing super-resolution classification map of low resolution HSI with less misclassification.

Collaborative representation plays an increasingly important role in the field of hyperspectral imagery target detection, resulting in improving detection performance. It is known that, in hyperspectral imagery, both the sensor and external factors (such as weather, illumination and other environmental changes) will lead to the spectral variations within the same type of material, which may greatly affect the detection accuracy. To deal with this issue, a new target detection method using collaborative representation with spectral variation extended dictionary is proposed for hyperspectral imagery in this paper. In the proposed method, an extended dictionary is constructed by enclosing the spectral variation library into the original dictionary, and the following collaborative representation makes the atoms in both original dictionary and spectral variation library contribute to the residual estimation. Compared to the traditional collaborative representation based target detection method, the newly proposed one exhibits better detection performance.

Sparse representation has been successfully used to solve target detection problem in hyperspectral images (HSI). Compared with the traditional target detection methods, it is not fully dependent on statistical structure of the data sets. In this paper, a hybrid sparsity and constrained energy minimization (HSCEM) detector for HSI is proposed. In sparse representation, local clustering or unmixing is used to obtain the dictionary, and the greedy subspace pursuit (SP) algorithm is used for sparse representation coefficient estimation. Combining sparsity-based detector with the traditional statistics-based detection method (CEM detector), the reconstructed result rather than reconstruction error is employed to distinguish between target and background. Experimental results illustrate the outperformance of the proposed HSCEM detector over several classic statistics-based detectors and sparsity-based detectors.