The reconstruction from three to dozens of spectral bands, known as spectral super resolution (SSR) has achieved remarkable progress with the continuous development of deep learning. However, the reconstructed hyperspectral images (HSIs) still suffer from the spatial degeneration due to the insufficient retention of high-frequency (HF) information during the SSR process. To remedy this issue, a novel Wavelet-based Hybrid Asymmetric Network (WHANet) is proposed to establish a RGB-to-HSI translation in wavelet domain, thus reserving and emphasizing the HF features in hyperspectral space. Basically, the backbone is designed in a hybrid asymmetric structure that learns the exact representations of decomposed wavelet coefficients in hyperspectral domain in a parallel way. Innovatively, a CNN-based HF reconstruction module (HFRM) and a transformer-based low frequency (LF) reconstruction module (LFRM) are delicately devised to perform the SSR process individually, which are able to process the discriminative wavelet coefficients contrapuntally. Furthermore, a hybrid loss function incorporated with the Fast Fourier loss (FFL) is proposed to directly regularize and emphasis the missing HF components. Eventually, experimental results over three benchmark datasets and one remote sensing dataset demonstrate that our WHANet is able to reach the state-of-the-art performance quantitatively and qualitatively.

A cycle-consistent sparse unmixing network based on deep image prior (C2SU-DIP) is proposed in this paper, to reduce the complexity of sparse unmixing (SU) algorithm and the loss of details in hyperspectral images (HSIs) simultaneously. In the proposed C2SU-DIP network, the complex design of regularization terms in sparse unmixing is avoided, meanwhile, details of abundances are effectively retained. It employs DIP-based sparse unmixing network as the backbone, and the learning process of the network replaces the regularization term design. Furthermore, cycle consistency is introduced by cascading two backbone networks, and a cycle consistency constrained loss function is designed for image detail preservation. Experimental results illustrate that the newly proposed C2SU-DIP network is capable of obtaining competitive unmixing results compared with several representative spectral unmixing methods.

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.

Dozens of spectral bands of hyperspectral images (HSIs) have been successfully reconstructed from only three color band images using deep neural networks according to their powerful nonlinear mapping capability. However, the existing deep-learning-based approaches tend to directly reconstruct HSIs from RGB inputs without emphasizing the discriminative intrinsic properties of different materials, resulting in certain distortion in reconstructed spectra. In this article, an intrinsic image decomposition (IID)-based spectral super-resolution (SSR) framework is proposed to reconstruct spectra of pixels from their reflectance feature and shading feature separately, by which the intrinsic properties can be emphasized during spectral reconstruction. Specifically, a dual hierarchical regression network (DHRNet) is designed for the proposed IID-based SSR task, in which a shading feature extraction module (SFEM) based on dense structure and a reflectance feature extraction module (RFEM) with attention mechanism are first, respectively, designed to reconstruct spectral information from reflectance feature and shading feature, and a feature enhancement module (FEM) is consequently devised to further improve the coarse combined estimation. Ultimately, a novel hybrid loss combining smooth$\boldsymbol {l}_{1}$loss, spectral angel mapper (SAM), and gradient prior is also presented to restrain the spectral distortion while enhancing the sharpness of the reconstructed HSI. Experimental results over three datasets demonstrate the superiority of our proposed framework.

Classification using multi-source remote sensing data has attracted widespread attention and is playing an increasingly important role in various fields. However, due to the disparity in imaging methods and the informational imbalance among data from various sources, it is still challenging to incorporate complementary advantages. Furthermore, real-world situations frequently involve varied data resolution, and the outcomes of straightforward super-resolution preprocessing are not always helpful for performing subsequent classification tasks. In this paper, we propose a unified framework for joint super-resolution and classification tasks of low resolution hyperspectral images (LR-HSIs) and SAR images. The minimization of the proposed joint loss function in the generative adversarial network (GAN) framework, including super-resolution and classification objective functions, can effectively achieve the multi-task goals. Experimental results on the super-resolution and classification of real LR-HSIs and SAR images demonstrate the effectiveness of the proposed method both visually and quantitatively.

Transformer has been widely applied in image processing tasks as a substitute for Convolutional Neural Networks (CNNs) for feature extraction due to its superiority in global context modeling and flexibility in model generalization. However, the existing transformer-based methods for semantic segmentation of Remote Sensing (RS) images are still with several limitations, which can be summarized into two main aspects: 1) the transformer encoder is generally combined with CNN-based decoder, leading to inconsistency in feature representations; 2) the strategies for global and local context information utilization are not sufficiently effective. Therefore, in this paper, a Global-Local Transformer Segmentor (GLOTS) framework is proposed for semantic segmentation of RS images to acquire consistent feature representations by adopting transformers for both encoding and decoding, in which a Masked Image Modeling (MIM) pretrained transformer encoder is adopted to learn semantic-rich representations of input images, and a multi-scale global-local transformer decoder is designed to fully exploit the global and local features. Specifically, the transformer decoder uses a feature separation-aggregation module (FSAM) to utilize the feature adequately at different scales and adopts a global-local attention module (GLAM) containing Global Attention Block (GAB) and Local Attention Block (LAB) to capture the global and local context information respectively. Furthermore, a Learnable Progressive Upsampling Strategy (LPUS) is proposed to restore the resolution progressively, which can flexibly recover the fine-grained details in the upsampling process. Experimental results on the three benchmark RS datasets demonstrate that the proposed GLOTS is capable of achieving better performance with some state-of-the-art methods, and the superiority of the proposed framework is also verified by ablation studies. The code will be available at https://github.com/lyhnsn/GLOTS.

Spectral super-resolution (SR), which reconstructs high spatial-resolution hyperspectral images (HSIs) from RGB inputs, has been demonstrated to be one of the effective computational imaging techniques to acquire HSIs. Though deep neural networks have shown their superiority in such a complex mapping problem, existing networks generally involve a very complex structure with huge amounts of parameters, resulting in giant memory occupation. In this article, a lightweight multiresolution feature fusion network (MRFN) is proposed, which adopts a multiresolution feature extraction and fusion framework to fully explore RGB inputs in different scales of resolution. Specifically, a lightweight feature extraction module (LFEM), which adopts cheap convolution and attention mechanisms, is constructed to explore different scales of features under a lightweight structure. Moreover, a hybrid loss function is proposed by encountering not only pixel-value level reconstruction error but also spectral continuity and fidelity. Experiments over three benchmark datasets, i.e., CAVE, Interdisciplinary Computational Vision Laboratory (ICVL), and NTIRE2022 datasets, have demonstrated that the proposed MRFN can reconstruct HSIs from RGB inputs in higher quality with fewer parameters and computational floating-point operations (FLOPs) compared with several state-of-the-art networks.

In recent years, convolutional neural networks (CNNs) have been successfully applied in hyperspectral image (HSI) classification tasks. However, the spatial-spectral features within an HSI have not been well explored using convolutions in CNNs. In the paper, a novel end-to-end hierarchical spatial-spectral transformer (HSST) is proposed for HSI classification, in which effective spatial-spectral features are emphasized using multi-head self-attention mechanism (MHSA). MHSA module captures better internal correlation of HSI data than the traditional convolution operation and can compute weighting scores for spatial and spectral context of pixels. Furthermore, a hierarchical architecture is designed to reduce a large number of parameters in the original transformer-style networks while still achieving satisfying classification results. Experimental results over two benchmark HSI datasets demonstrated the proposed HSST obviously outperforms several state-of-the-art deep learning-based HSI classification algorithms.

Spectral super-resolution (SR), which generally reconstructs hyperspectral images (HSIs) from RGB inputs, has attracted lots of attention recently. In this paper, a spectral SR algorithm based on intrinsic image decomposition (IID) is proposed, in which RGB images are decomposed into reflectance images and shading images to fully explore RGB features for HSI reconstruction. Considering that features of the reflectance image are only related to the material of objects, the sparsity of material reflectivity is used to reconstruct the reflectance image of HSI. Moreover, an convonlutional neural network (CNN) is constructed to reconstruct shading parts of HSI. Finally, these two reconstructed results are fused to generate the high spectral resolution HSI and an enhancement network is also designed to further improve the recontruction performance. Experimental results with two benchmark datasets, ICVL and CAVE, demonstrate that the performance of the proposed algorithm is superior to several state-of-the-art spectral SR algorithms.

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.

Convolutional neural networks (CNNs) have achieved excellent feature extraction capabilities in remotely sensed hyperspectral image (HSI) classification. This is due to their ability to learn representative spatial and spectral features. However, it is difficult for conventional computers to classify HSIs quickly enough for practical use in many applications, mainly because of the large number of calculations and parameters needed by deep learning-based methods. Although several weight quantization methods achieved remarkable results in network compression, the network acceleration effect is still not significant because a full exploration of the potential of network acceleration brought by network weight quantization is still absent from the literature. In this article, a new step activation quantization method is proposed to constrain the input of the network layer of the CNN so that the data can be represented by low-bit integers. As a result, floating-point operations can be replaced with integer operations to greatly accelerate the forward (inference) step of the network. Specifically, nonlinear uniform quantization is adopted in this work to restrain the input of the CNN in the forward inference of the step activation quantization layer, and two functions (constant and tanh-like) are used in the backpropagation step to avoid gradient vanishing and noise. Our newly proposed step activation quantization acceleration method is applied to a CNN for HSI with two well-known benchmark data sets and the experimental results demonstrate that the proposed method is very effective in terms of both memory savings and computation acceleration, with only a slight decrease in classification accuracy. Specifically, our method reduces memory requirements in$13.6\times $and obtains around$10\times {}$speedup with regard to the original real-valued network version.

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.

Reconstructing hyperspectral images from RGB inputs has gained great attention recently. In dictionary representation-based hyperspectral image reconstruction, dictionary representation is first carried out in RGB space and then dictionary reconstruction is conducted in hyperspectral space for per-pixel reconstruction. However, such work mainly focuses on spectral mapping from RGB space to hyperspectral space, ignoring physical distribution of objects in the image. In this paper, spatial context of pixels is used to improve the reconstruction performance. Specially, neighboring pixels are used to constrain the dictionary representation problem in RGB space, and the Simultaneous Orthogonal Matching Pursuit (SOMP) is used to improve the performance of hyperspectral reconstruction. Experimental results on two benchmark data sets demonstrate the superiority of the proposed technique.

Noises in hyperspectral image (HSI) degrades both spatial and spectral features of ground objects, and greately defects the following processing, such as classification, target detection and recognition. In this paper, a convolutional self-encoding network (DeCS-Net) is designed for HSI denoising, which integrates the superiority of convolutional neural network (CNN) and auto-encoder (AE) to learn multi-scale features. The noise in the observed HSI is estimated by residual learning strategy, and is removed from the observed HSI to obtain an estimation of the ideal HSI without noise. Experimental results on benchmark HSI data set illustrate that the proposed DeCS-Net is effective for HSI denoising and outperforms the state-of-the-art CNN based HSI denoising methods.

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.

convolutional neural network (CNN) has been widely used for hyperspectral classification. Current researches of CN-N based hyperspectral image classification is mainly implemented on graphics processing unit (GPU) platform. However, GPU is not suitable for onboard processing due to the problem of space radiation and power supply on image acquiring platform. Therefore, in this paper, FPGA is selected to implement CNN based hyperspectral classification for further onboard processing. Specially, a hardware model is designed for the forward classification step of CNN using hardware description language, including computation structure for CNN, implementation of different layers, weight loading scheme, and data interfere. Simulation results over Pavia data set validate the proposed FPGA based implementation is coincide with that on GPU platform.

Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of high-dimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.

This paper presents a new dictionary-based method to fuse hyperspectral image (HSI) and multispectral image (MSI) of the same observed scene. To incorporate spatial as well as spectral information and features from both source HSI and MSI, dual-source spatial-spectral dictionary pair is constructed. Furthermore, collaborative representation based image representation and reconstruction is employed for its outstanding representation performance and efficiency. Simulative experiments illustrate that the newly proposed fusion method is capable of producing better or comparable fusion result when compared to some state-of-the-art dictionary-based HSI and MSI fusion methods, with much less computational cost.

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.

In this paper, a new subpixel mapping approach for hyperspectral image is proposed, using a spatial-spectral endmember dictionary with collaborative representation (CR). Different from the classic approaches, the proposed approach employ several spatially closest training samples as the endmembers used for the representation of each mixed pixel, instead of the entire training set. Furthermore, the CR coefficients are derived from the CR of the mixed pixel using the entire training set. Simulative experiments illustrate its outperformance over several classic approaches.

Hyperspectral image (HIS) classification is a hot topic in remote sensing community and most of the existing methods extract the features of original Hyperspectral data using shallow layer networks such as neural network (NN) and support vector machine (SVM). As deep learning recently achieves great success in machine learning and pattern recognition area for its ability in deep feature extraction and representations, two deep networks i.e. deep convolutional network (DCN) and deep belief network (DBN) have been used for hyperspectral image classification and better results have been achieved. Differing from those deep networks for HSI classification, in this paper, we propose a new method for hyperspectral image classification based on deep stacking network (DSN), which owns advantages to other deep models for its simplicity when processing in batch-mode learning - not requiring stochastic gradient descent that other DNNs require. The feature extraction is gradually obtained by employing nonlinear activation function on the hidden layer nodes of each module, which is different from those DSNs that usually use linear weights between the hidden layer and the output layer. Experimental results on AVIRIS hyperspectral images show that the proposed method achieves improved classification performance when compared with that via SVM and NN methods.

Subpixel mapping with a low resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image with high spatial resolution is not always available in practice. In this paper, to extract spatial information without auxiliary image, the upscaled low resolution hyperspectral image is classified using collaborative representation-based classifier. Another subpixel scale classification map is available by the combination of collaborative representation-based classification, spectral unmixing and subpixel spatial attraction model. To achieve better classification performance, decision fusion is employed to elect approximate class label from these two initial classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed approach is more promising in extracting and utilizing spatial information compared with some state-of-the-art subpixel mapping approaches.

In this paper, the spatial resolution of hyperspectral image (HSI) is enhanced by fusing it with multispectral image (MSI) of the same scene with a higher spatial resolution. The w-hole spectrum covered by HSI channels is divided into several regions according to MSI spectral channels. The HSI-MSI fusion problem is then simplified by fusing images of each spectral region one after another. Specifically, a fusion algorithm based on collaborative representation (CR) with local adaptive dictionary pair is proposed. Compared to the classic global dictionary, the scale of the local adaptive one is much smaller such that the related computational cost is also reduced. The employment of CR is capable of reducing the reconstruction error to guarantee an improved fusion performance. Simulative experiments are deployed for illustration and comparison.

Hyperspectral (HS) remote sensing image with finer spectral information has great advantages in feature identification and classification. However, the spatial resolution of HS image is usually low due to practical limitations. In this paper, the low-spatial-resolution HS image is fused with the high-spatial-resolution multispectral (MS) image of the same observation scene to improve its spatial resolution. A novel spectral unmixing based HS and MS image fusion approach (VSC-CNMF) is proposed, in which CNMF with minimum endmember simplex volume and abundance sparsity constraints is employed for coupled unmixing of HS and MS images. Simulative experiments are employed for verification and comparison. The experimental results illustrate that the newly proposed VSC-CNMF based HS and MS fusion algorithm outperforms several state-of-the-art unmixing based fusion approaches in cases with moderate number of endmembers.

In order to enhance the spatial resolution of the hyperspectral images, a novel fast algorithm based on Spectral Mixture Analysis (SMA) techniques is proposed for the fusion of coarse-resolution hyperspectral (HS) image and high-resolution multispectral (MS) image. The high-resolution hyperspectral image is synthesized by integrating high-resolution spectral information of hyperspectral image represented by endmembers and high-resolution spatial information of multispectral image represented by abundance. As a result, a novel SMA based diagram is designed, in which Endmember Extraction (EE) is performed on hyperspectral images while Abundance Estimation is performed on multispectral images, and the unmixing process in these two images are matched by utilizing the spectral response matrix and the spatial spread transform matrix in the observation model. Finally, real HYDICE data experiments are utilized to demonstrate the effectiveness of the proposed fusion algorithm.

In this paper, a Bayesian restoration technique for multiple observations of hyperspectral (HS) images is presented. As a prototype problem, we assume that a low-spatial-resolution HS observation and a high-spatial-resolution multispectral (MS) observation of the same scene are available. The proposed approach applies a restoration on the HS image and a joint fusion with the MS image, accounting for the joint statistics with the MS image. The restoration is based on an expectation-maximization algorithm, which applies a deblurring step and a denoising step iteratively. The Bayesian framework allows to include spatial information from the MS image. To keep the calculation feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for general HS image restoration and for the specific case of pansharpening. The experimental results of the proposed approach are compared with pure fusion and deconvolution results for performance evaluation.

In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multi-spectral (MS) image by accounting for the joint statistics. Particularly, a zero-mean heavy-tailed model, Gaussian Scale Mixture (GSM) model, is employed as the prior, which is believed to be capable of modelling the distribution of wavelet coefficients more accurately than traditional Gaussian model. To keep the calculations feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for both general HS and MS image fusion as well as the specific case of pansharpening. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian prior for performance evaluation.

Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial–spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend to neglect anomalous endmembers that are also of interest. Therefore, in this paper, an improved SSEE (iSSEE) algorithm is proposed to address such limitation of conventional SSEE algorithms by accounting for both anomalous and normal endmembers. By developing simplex projection and simplex complementary projection, all the hyperspectral pixels are projected into a simplex determined by the normal endmembers extracted in conventional SSEE algorithms. As a result, anomalous endmembers are identified iteratively by utilizing the$l_{2}^{\infty}$norm to find the maximum simplex complementary projection. In order to determine how many anomalous endmembers are to be extracted, a novel Residual-be-Noise Probability-based algorithm is also proposed by elegantly utilizing the spatial-purity map generated in the previous SSEE step. Experimental results on both synthetic and real datasets demonstrate that simplex projection errors can be significantly reduced by identifying both anomalous and normal endmembers in the proposed iSSEE algorithm. It is also confirmed that the performance of the proposed iSSEE algorithm clearly outperforms that of SEE algorithms since both spatial context and spectral similarity are utilized.

In this paper, a restoration technique for hyperspectral images is presented. The technique requires a low spatial resolution hyperspectral image and a high spatial resolution multispectral image of the same scene. The proposed approach applies a restoration on the hyperspectral image, while accounting for the joint statistics with the multispectral image. The restoration is based on an Expectation-Maximization algorithm, which applies a deconvolution step and a denoising step iteratively. A practical implementation scheme is presented. Simulation experiments are conducted for performance evaluation.

In this paper, we present a combined image restoration and fusion approach to enhance the spatial resolution of hyper-spectral (HS) images, using a low spatial resolution HS observation and a high spatial resolution multispectral (MS) observation of the same scene. The proposed approach is based on an iterative Expectation-Maximization restoration algorithm, improving the spatial resolution of the HS observation by knowledge of the images point spread function, combined with a Bayesian fusion approach, using the MS observation at a higher spatial resolution scale as an auxiliary. A practical implementation scheme is presented. Simulation experiments are conducted for performance evaluation.

In this paper, a technique is presented for the fusion of multispectral (MS) and hyperspectral (HS) images to enhance the spatial resolution of the latter. The technique works in the wavelet domain and is based on a Bayesian estimation of the HS image, assuming a joint normal model for the images and an additive noise imaging model for the HS image. In the complete model, an operator is defined, describing the spatial degradation of the HS image. Since this operator is, in general, not exactly known and in order to alleviate the burden of solving the inverse operation (a deconvolution problem), an interpolation is performeda priori. Furthermore, the knowledge of the spatial degradation is restricted to an approximation based on the resolution difference between the images. The technique is compared to its counterpart in the image domain and validated for noisy conditions. Furthermore, its performance is compared to several state-of-the-art pansharpening techniques, in the case where the MS image becomes a panchromatic image, and to MS and HS image fusion techniques from the literature.

In this work, a technique is presented for the fusion of multi-spectral (MS) and hyperspectral (HS) images to enhance the spatial resolution of the latter. The technique works in the wavelet domain, and is based on a Bayesian estimation of the HS image, assuming a joint normal model for the images, and an additive noise imaging model for the HS image. An appropriate estimation strategy is also proposed. The technique is compared to its counterpart in the spatial domain, and validated for noisy conditions. Further, its performance is compared to several state-of-the-art pansharpening techniques, in the case where the MS image becomes a panchromatic image, and to some MS and HS image fusion techniques from the literature.