As a kind of low-dimensional visual structural feature, edge helps to highlight the basic information of the image, playing the key role in the pre-processing of subsequent advanced visual tasks. Edge detection models with VGG16 as the basic framework can achieve excellent performance through transfer learning, but such models suffer from problems such as large number of parameters and high computational costs. To address the challenge of the coexistence of accuracy and lightweight, an edge detection network inspired by the characteristics of biological visual receptive fields (BVRF-Net) was proposed in the paper. In the Global Pathway, the ON/OFF type convolution kernel was constructed by simulating the ganglionic ON/OFF centroid type receptive field, initializing the convolution kernel by pixel difference value. Meanwhile, simulating the sparse suppression property of complex neurons, the sparse surround suppression convolution kernel with center periphery adjustment was constructed to suppress the texture noise. In the Local Pathway, the asymmetric orientation selectivity convolution kernel was constructed by simulating the orientation selectivity and asymmetric surround suppression characteristics of primary visual cortex neurons, which helps to quickly optimize the orientation features and enhance the contrast features by giving the convolution kernel a priori knowledge, achieving the precise extraction of local detail features. Taking BSDS500 dataset, NYUD-v2 dataset and Multicue dataset as experimental objects, BVRF-Net can achieve competitive results with only 0.358M parameters required. The excellent performance proves the effectiveness of incorporating bio-vision characteristics to construct neural network models, promoting the development of biomimetic computational vision.

Cross-resolution change detection (CRCD) is of significant practical importance in disaster assessment, rapid urban transitions, and various applications. Conventional change detection methods are primarily tailored for bitemporal images with consistent spatial resolution, rendering them unsuitable for direct application to CRCD tasks. This limitation stems from the substantial scale differences and pixel-wise misalignment prevalent in cross-resolution remote sensing images. In response to these challenges, we introduce a semantic-aware alignment network (SA-Net). SA-Net utilizes cross-attention to map bitemporal images into a shared semantic space, effectively alleviating the difficulties of the subsequent alignment arising from semantic mismatches. Furthermore, a joint transformer featuring an encoder-decoder architecture is employed to extract global information and learn the geometric parameters for spatial alignment between bitemporal images. Experimental evaluations on two real-collected datasets, HTCD and MRCDD, showcase the superior performance of our proposed SA-Net in CRCD tasks.

The small-sample-size problem of hyperspectral image (HSI) classification has recently gained considerable attention. Cross-scene HSI classification has emerged as an effective solution to this problem. In real-world applications, different HSI scenes are often captured by diverse sensors, resulting in variations between scenes. Graph modeling, as a method to represent relationships, leverages semantic information to establish connections between scenes, thereby facilitating transfer learning by aligning their features. However, in scenarios with only a few labeled target samples, the resulting graph is typically sparse and can only capture weak cross-scene relationships. Studies have shown that a dense and fault-tolerant graph is beneficial for transfer learning in small-sample-size cases. Consequently, we propose a novel heterogeneous transfer learning approach called adaptive graph modeling with self-training (AGM-ST). Unlike conventional graph modeling methods that employ predefined graph weights, adaptive graph modeling (AGM) employs a learnable network to generate graph weights based on the similarities of spectral–spatial features. Additionally, an adaptive cutoff threshold is trained to eliminate weak relationships between samples that may be potentially incorrect. Subsequently, a cross-scene graph loss is designed based on the generated graph to align the feature spaces of the source and target scenes. Furthermore, the unlabeled samples from the target scene are gradually updated with pseudo labels using the self-training (ST) technique, which enhances semantic information and improves graph modeling. Experimental evaluations conducted on three cross-scene HSI datasets have demonstrated the effectiveness of the proposed AGM-ST approach.

The transformer has been introduced in the hyperspectral image (HSI) classification, demonstrating outstanding capability in capturing global features compared to the convolutional neural network (CNN). However, the small-sample-size problem poses a significant challenge in practical HSI classification, especially in training the transformer. To tackle this issue, cross-domain transfer learning is adopted as a practical solution, which transfers the information from a source domain with abundant labeled samples to a target domain with limited labeled samples. This article proposes a novel transfer learning method for heterogeneous cross-domain HSI classification called cross-domain discriminative vision transformer (CD-DViT). This algorithm primarily contains three key contributions. First, source samples are mapped to the target domain through an encoder-decoder architecture, and the mapped source samples can be used to train the target classifier. Second, the cross-attention mechanism is utilized to construct two blocks for achieving the domainwise and classwise feature alignments (FAs), respectively. Specifically, the combination of the cross-attention mechanism with the domain discriminator aims to learn domain-invariant features, thereby facilitating domainwise alignment and alleviating domain shift. Third, knowledge distillation (KD) is adopted to learn more information from the target domain and assist in classifying target samples. Our experiments on three real-world cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.

The small-sample-size issue in hyperspectral image (HSI) classification remains a significant challenge. To improve the classification accuracy of a dataset with a few labeled samples (target domain), we can use knowledge from another dataset with sufficient labeled samples (source domain). This is called cross-domain HSI classification. Transfer learning enables knowledge transfer between source and target domains. Different HSI datasets are often acquired by different sensors, resulting in different characteristics. Consequently, different HSIs possess different feature spaces, and knowledge transfer between them becomes difficult due to heterogeneities. CycleGAN, based on adversarial learning, can help solve heterogeneous transfer learning tasks by establishing the two-way mapping between two different feature spaces. However, CycleGAN contains only one cycle for data, leading to large mapping errors. This article proposes a novel CycleGAN-based transfer learning method for cross-domain HSI classification. The proposed method extends the two-way mapping of CycleGAN. It incorporates multiple mapping cycles to construct a multi-CycleGAN, which is then unfolded to derive the Cross-Domain Mapping Chain (CDMC) model. The generators in our proposed CDMC provide accurate mappings between domains. Moreover, we calculate and accumulate errors in each cycle, and the backpropagation of accumulated errors through the chains improves the model’s performance. Besides, auxiliary classifiers are introduced to account for class-conditional distributions in the mapping process. Experimental results on three real-world heterogeneous cross-domain HSI datasets show the effectiveness of the proposed method.

A major challenge in hyperspectral image (HSI) classification is the small-sample-size problem. Cross-domain information can help solve the problem. In cross-domain HSI classification, the source domain has many samples, while the target domain has fewer samples. Transfer learning can transfer knowledge from the source domain to the target domain. The source and target domains are mostly captured by different sensors and thus come from different feature spaces. Heterogeneous transfer learning can solve this problem. This paper proposes a transfer learning method based on a crossdomain graph convolutional network (CD-GCN). A class co-occurrence semantic graph is built between heterogeneous spaces of source and target domains. Then graph convolutional network (GCN) is adopted to learn the features of graphs. To handle the different feature dimensions, a feature alignment subnet is proposed. By combining a feature alignment subnet and a GCN feature extraction subnet, the proposed model CD-GCN transfers knowledge between heterogeneous domains. Experiments on two cross-domain HSI datasets prove that CD-GCN overperforms many transfer learning methods.

Small-sample-size problem is a big challenge in hyperspectral image (HSI) classification. Deep learning-based methods, especially Transformer, may need more training samples to train a satisfactory model. Cross-domain classification has been proven to be effective in handling the small-sample-size problem. In two HSI scenes sharing the same land-cover classes, one with sufficient labeled samples is called the source domain, while the other with limited labeled samples is called the target domain. Thus, the information on the source domain could help the target domain improve classification performance. This paper proposes a cross-domain Vision Transformer (CD-ViT) method for heterogeneous HSI classification. CD-ViT maps the source samples to the target domain for supplementing training samples. In addition, cross-attention is used to align the source and target features. Moreover, knowledge distillation is employed to learn more transferable information. Experiments on three different cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.

Abstract Small‐sample‐size problem is always a challenge for hyperspectral image (HSI) classification. Considering the co‐occurrence of land‐cover classes between similar scenes, transfer learning can be performed, and cross‐scene classification is deemed a feasible approach proposed in recent years. In cross‐scene classification, the source scene which possesses sufficient labelled samples is used for assisting the classification of the target scene that has a few labelled samples. In most situations, different HSI scenes are imaged by different sensors resulting in their various input feature dimensions (i.e. number of bands), hence heterogeneous transfer learning is desired. An end‐to‐end heterogeneous transfer learning algorithm namely domain‐invariant attention network (DIAN) is proposed to solve the cross‐scene classification problem. The DIAN mainly contains two modules. (1) A feature‐alignment CNN (FACNN) is applied to extract features from source and target scenes, respectively, aiming at projecting the heterogeneous features from two scenes into a shared low‐dimensional subspace. (2) A domain‐invariant attention block is developed to gain cross‐domain consistency with a specially designed class‐specific domain‐invariance loss, thus further eliminating the domain shift. The experiments on two different cross‐scene HSI datasets show that the proposed DIAN achieves satisfying classification results.

Data-driven deep learning (DL)-based methods directly learn the nonlinear mapping between noisy hyperspectral images (HSIs) and corresponding clean ones. However, DLbased methods neglect the prior knowledge of HSIs embodied by physical models. Consequently, they require complex network architectures and a large number of training samples. To address the above issues, this paper introduces a multitask sparse neural network (MTSNN) which bridges the sparsity prior of HSIs with data-driven deep learning for HSI denoising. Specifically, we first build a multitask sparse (MTS) denoising model which shares sparse coefficients among bands to exploit the spectral-spatial correlation and learns a dictionary for each band to depict the distinct spatial structure among bands. The iterative optimization of the MTS model is then unfolded to yield our MTSNN by introducing some learnable parameters. MTSNN is a multi-branch network. Each branch performs a single denoising task for an individual band. All branches are connected by shared coefficients, forming multitask denoising for all bands. The hybrid advantages of the MTS model and data-driven learning equip MTSNN with strong denoising ability, preferable learning capability, superior interpretability, and higher generalization capacity. Experimental results demonstrate that our method achieves state-of-the-art denoising performance compared with several alternative approaches.

Lack of labeled training samples is a challenge in hyperspectral image (HSI) classification. Cross-scene classification is a valid solution to few-shot learning problem. In cross-scene classification, two strongly related HSI scenes are considered, one with sufficient labeled samples is called source scene, while the other one containing limited labeled samples is called target scene. By establishing connections between two scenes, abundant labeled samples in source scene can benefit the classification of target scene. In this paper, a novel model named cycle auxiliary classifier generative adversarial network (Cycle-AC-GAN) is proposed for heterogeneous transfer learning across source and target scenes. In Cycle-AC-GAN, a source-to-target generator and a target-to-source generator are simultaneously built. Thus, a two-way mapping can be effectively established between source and target scenes with the adversarial training. In addition, different from existing CycleGAN, in Cycle-AC-GAN, each discriminator contains a binary domain classifier and an auxiliary land-cover classifier. The auxiliary classifiers can align the class-conditional distributions between source and target HSIs. Inspiring experimental results on two real-world cross-scene HSI datasets demonstrate the effectiveness of the proposed approach.

Expensive cost of labeling leads to few-shot learning problem in hyperspectral image (HSI) classification. Cross-scene classification is a novel approach to solve this problem. In this work, we propose an end-to-end heterogeneous transfer learning algorithm namely cross-domain attention network (CDAN) to settle the cross-scene classification problem. CDAN mainly contains two modules. 1) A two-stream HybirdSN architecture is designed for extracting features from source and target scenes, aiming at projecting the features into a shared low-dimensional subspace. 2) Cross-domain attention mechanism is adopted based on the consistency of features between different scenes. A cross-domain updating rule is proposed for training the subnet. CDAN is proved to be effective according to the experiments on two different cross-scene HSI datasets.

Sparse representation (SR) is a widely accepted hyper-spectral image (HSI) denoising model. Because of the curse of dimensionality and the desire to better fit the data, the SR models are typically deployed on small and fully overlapping blocks whose results are averaged to produce the global de-noised HSI. This “local-global” denoising mechanism ignores the dependencies between blocks, resulting in visual artifacts. This paper describes the underlying clean HSI with a 3D con-volutional sparse coding (CSC) model, representing the HSI with a linear combination of few shift-invariant 3D spatial-spectral filters in a global dictionary. Instead of operating on patches, the CSC model sees the clean HSI is generated from a sum of local atoms that appear in a small number of locations throughout the image, naturally retaining the relationship between pixels. Moreover, we unfold the optimization process of the model into a spatial-spectral convolutional sparse neural network which absorbs the interpretation ability of the model while supporting discriminative learning from data. Experimental results on both synthetic and real-world datasets show that our network achieves competitive denoising performances, qualitatively and quantitatively.

Deep learning-based methods have achieved remarkable results in the field of hyperspectral image (HSI) denoising, and these methods are typically trained on pairs of noisy input and clean target images. How to deal with the noise in real-world HSIs when clean targets are unavailable is still a challenging problem. In this paper, we propose a self-supervised HSI denoiser in which only a single noisy HSI is utilized. We exploit the blind-spot network and extend the method in the spatial-spectral space to accomplish self-supervised learning. In order to better extract spatial-spectral features with limited training samples, we use separable feature extraction modules to extract spectral-spatial joint information of HSI separately and finally fuse these features. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms some state-of-the-art denoising approaches.

Hyperspectral image (HSI) classification is a small-sample-size problem due to the expensive cost of labeling. As a novel approach to this problem, cross-scene HSI classification has become a hot research topic in recent years. In cross-scene HSI classification, the scene containing enough labeled samples (called source scene) is used to benefit the classification in another scene containing a small number of training samples (called target scene). Transfer learning is a typical solution for cross-scene classification. However, many transfer learning algorithms assume an identical feature space for source and target scenes, which violates the fact that source and target scenes often lie in different feature spaces with various dimensions due to different HSI sensors. Aiming at the different feature spaces between the two scenes, we propose an end-to-end heterogeneous deep transfer learning algorithm, namely, cross-domain variational autoencoder (CDVAE). This algorithm is mainly composed of two key parts: 1) the features of the two scenes are embedded into the shared feature subspace through the two-stream variational autoencoder (VAE) to ensure that the output feature dimensions of the two scenes are identical and 2) graph regularization is used to establish the manifold constraints between source and target scenes in the shared subspace, so as to align the feature spaces. Experiments on two different cross-scene HSI datasets have proved the superior performance of the proposed CDVAE algorithm.

Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming. Learning-based methods perform unmixing in a data-driven manner but heavily rely on the quality and quantity of the training samples due to the lack of physical interpretability. In this paper, we combine the advantages of both model-based and learning-based methods and propose a nonnegative matrix factorization (NMF) inspired sparse autoencoder (NMF-SAE) for hyperspectral unmixing. NMF-SAE consists of an encoder and a decoder, both of which are constructed by unrolling the iterative optimization rules of L1sparsity-constrained NMF for the linear spectral mixture model. All parameters in our method are obtained by end-to-end training in a data-driven manner. Our network is not only physically interpretable and flexible but also has higher learning capacity with fewer parameters. Experimental results on both synthetic and real-world data demonstrate that our method is capable of producing desirable unmixing results when compared against several alternative approaches.

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.

We present a novel feature extraction method to improve classification of hyperspectral image, leveraging graph wavelet transform to address the shortcomings of classical wavelet transform that only works on a regularly 2-D or 3-D grid. In the proposed method, we first design an appropriate underlying graph connecting pixels with weights that reflect the hyperspectral image structure; secondly interpret the hyperspectral image as a signal on graph, and apply graph wavelet transform for processing hyperspectral images in graph spectral domain; finally, the transformed hyperspectral images are used to constitute the pixel features, and input these features to a classifier to get the class label. Graph can represent the spectral similarity and nonlocal spatial relation, moreover graph wavelets are sparse and localized in vertex/pixel domain, offering spectral-spatial intrinsic and discriminative features. The experimental results show the competitive performance compared with classical wavelet features.

In real-world applications of hyperspectral images (HSIs), we may frequently face the following situation: two HSI scenes (named source and target scenes, respectively) contain similar land over objects, but they are captured in different spots or at different time. Even if they are captured by the same hyper-spectral sensor, there exist spectral shift between them. In our previous work, we tried to solve the spectral shift by dictionary sharing or domain-invariant feature selection. However, a more regular case is that two similar HSI scenes are captured by different hyperspectral sensors. How to mine the relationship between such HSIs is a more challenging problem, since the feature spaces are totally different. A natural approach is to learn a unified low-dimensional feature subspace which can bridge the two HSI scenes. In this work, we propose a dual dictionary nonnegative matrix factorization (DDNMF) algorithm for the aforementioned goal. In details, an individual domain-specific dictionary is learned for each scene, and two dictionary learning tasks (for source and target scenes) are coupled by manifold regularization, ensuring that pixels belonging to a same land cover class have similar representations over the learned dictionaries, even if they come from different scenes. Experimental results show that the proposed algorithm can indeed mine a unified feature subspace shared between two different HSI scenes.

Feature extraction is an important research topic in hyper-spectral image (HSI) classification. However, most of feature extraction methods only extract low-level features, which makes them not perform well in the applications of HSI. In this paper, we have proposed a non-negative matrix factorization (NMF) based deep feature extraction algorithm, namely deep NMF. Deep NMF tries to construct a deep feature representation by cascading multiple NMFs. Reconstruction residual of NMF is passed layer by layer to reduce information loss. Meanwhile, passing residuals between layers can construct a feature hierarchy from coarse to fine. Furthermore, activation functions are applied between adjacent layers to enhance the ability of non-linear feature extraction. Experimental results have also shown that our algorithm is computationally efficient and effective for HSI classification.

Hyperspectral images (HSIs) are always corrupted by noise, which will strongly affect the applications. Various denoising algorithms have been proposed for HSIs. Most existing denoising methods have parameters related to intensity of noise. So noise estimation is an essential step in HSI denoising. In our previous work, we have proposed a homogeneous region based noise estimation algorithm. However, we find it often fails on severely corrupted bands. To solve the problem, two improvements are made in this work: 1) depending on strong correlations between bands, a regression based signal-noise separation is adopted; 2) utilizing the identical spatial structure of different bands, a unified homogeneous region segmentation is performed across all bands via clustering of spectral vectors. Then, noise estimation is done using curve fitting according to the segmented homogeneous regions with separated signal and noise components. By this spectral-spatial joint approach, we have significantly improved the accuracy of noise estimation.

BACKGROUND: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. RESULTS: In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. CONCLUSIONS: Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral data sets show the superiority of the proposed method.

Feature selection is an important research topic for hyperspectral images (HSIs). It helps to remove the noisy or redundant features. Traditional feature selection algorithms are mostly performed within a single HSI scene (dataset). However, appearance of massive HSIs requires the feature selection problems to be considered across different HSI scenes, e.g., two HSI scenes obtained from different spots or at different time. In this case, the features are not identically distributed within two scenes due to spectral shift. To solve this problem, a cross-scene feature selection algorithm is proposed in this work for HSIs, which is based on cross-domain information gain (CDIG). The main motivation includes two factors, one is the discriminant of selected features to separate different land-cover classes, while the other is the consistency of the selected features between different scenes. Consequently, the proposed CDIG reaches a compromise between aforementioned two factors. Experimental results on two cross-scene HSI datasets show the advantages of the proposed CDIG in cross-scene feature selection problems.

In the above paper[1], there is an error inFig. 14.Fig. 14should include$3\times3$matrices rather than$7\times7$, since the Shanghai-Hangzhou dataset has three land-cover classes. The corrected figure appears here.

A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification.

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Hyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, among many methods dedicated to the problem, has attracted much attention and showed state-of-the-art performance. Due to the low-rank property of natural images, an assumption can be made that the latent clean signal is a linear combination of a minority of basis atoms in a dictionary, while the noise component is not. Based on this assumption, denoising can be explored as a sparse signal recovery task with the support of a dictionary. In this paper, we propose to solve the HSI denoising problem by sparse nonnegative matrix factorization (SNMF), which is an integrated model that combines parts-based dictionary learning and sparse coding. The noisy image is used as the training data to learn a dictionary, and sparse coding is used to recover the image based on this dictionary. Unlike most HSI denoising approaches, which treat each band image separately, we take the joint spectral-spatial structure of HSI into account. Inspired by multitask learning, a multitask SNMF (MTSNMF) method is developed, in which bandwise denoising is linked across the spectral domain by sharing a common coefficient matrix. The intrinsic image structures are treated differently but interdependently within the spatial and spectral domains, which allows the physical properties of the image in both spatial and spectral domains to be reflected in the denoising model. The experimental results show that MTSNMF has superior performance on both synthetic and real-world data compared with several other denoising methods.

Noise reduction for hyperspectral imagery (HSI) is an indispensable step before further processes such as object detection and classification. In this paper, we propose a noise reduction method for HSI based on non-local strategy and tensor factorization. Based on the observation that natural images are always locally self-repetitive, we divide the whole HSI into small sub-blocks and cluster similar blocks into groups. Since similar blocks share the same underlying structure, the redundancy can be utilized to remove noise of the blocks jointly. We stack the similar blocks to construct a fourth-order tensor from each group. Noise is reduced by finding the lower dimensional approximation of each of the fourth-order tensors via Tucker factorization. The experimental results indicate that the proposed method has a good quality of restoring the true signal from the noisy observation.

Displaying the abundant information contained in a hyperspectral image is a challenging task. Previous visualization approach focused only on preserving the structure in the original images. They ended up with presenting pseudo-color images and stopped short of adjusting the color of the images to retrieve more desirable visual effects. In this paper, a new visualization algorithm is proposed. It can be modeled as a two stage approach. At the first stage, Laplacian Eigenmaps algorithm is applied to reduce the dimension of the hyperspectral image. In this way we obtain a three dimensional image with pseudo-color. At the second stage, we transfer the natural color of a panchromatic image to the image obtained by the first step via manifold alignment. Experimental results show that the visualized image not only retains the structure of the hyperspectral image but also possesses natural colors.

It is extensively accepted that much noise is included in hyperspectral imagery (HSI). Noise removal for HSI is an important but challenging task. Most denoising methods have one or more model parameters. For many algorithms, the denoising performance strongly depends on the values of parameters. In many cases, empirically selected parameters are not adaptive to various noise levels. Another challenge is the computational complexity. Since HSI has numerous bands, band by band HSI denoising is relatively time-consuming when compared to RGB or gray image. So a fast algorithm is preferred in practice. In this work, a multi-task orthogonal matching pursuit (MT-OMP) algorithm is proposed for ℓ2,0non-local sparse denoising. This greedy scheme is a multi-task extension of the famous OMP algorithm. The only parameter of MT-OMP is the sparse reconstruction error, which can be derived via noise variance. Furthermore, it is time-efficient and easy to implement. The experimental results show advantages of the proposed MT-OMP algorithm.

Sparse coding based noise reduction algorithms have been extensively applied on hyperspectral imagery (HSI) denoising. Dictionary learning schemes are strongly suggested for sparse reconstruction in many researches, aiming at a smaller error between the underlying clean image and the reconstruction result. In previous researches, the training samples (patches) are selected from either unrelated clean images or the noised image itself. The dictionaries learned form unrelated clean images can not perfectly represent the underlying clean target image, while the dictionaries learned form the noised image itself may be affected by the noise existing in training samples. In this paper, we propose a novel dictionary learning scheme that depends on a panchromatic image from the same or similar scene with HSI. Considering the fact that the noise level of a panchromatic image is always much lower than HSI, we take the patches from panchromatic image as training samples. Taking the multi-scale image representation into consideration, we construct the dictionary from different scales via Gaussian pyramid. The proposed dictionary shows its good denoising performance in our experiments.

Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enable more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training samples have caused some difficulties in the classification, such as overfitting in learning, noise sensitiveness, overloaded computation, and lack of meaningful physical interpretability. In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and 3-D discrete wavelet transform (3D-DWT) texture features. The 3D-DWT decomposes a hyperspectral data cube at different scales, frequencies, and orientations, during which the hyperspectral data cube is considered as a whole tensor instead of adapting the data to a vector or matrix. This allows the capture of geometrical and statistical spectral-spatial structures. After the feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables to model the data for regression and classification purpose. A linear structured sparse logistic regression model is proposed to simultaneously select the discriminant features from the pool of 3D-DWT texture features and learn the coefficients of the linear classifier, in which the prior knowledge about feature structure can be mapped into the various sparsity-inducing norms such as lasso, group, and sparse group lasso. Furthermore, to overcome the limitation of linear models, we extended the linear sparse model to nonlinear classification by partitioning the feature space into subspaces of linearly separable samples. The advantages of our methods are validated on the real hyperspectral remote sensing data sets.

Noise reduction is one of important processing tasks for hyperspectral imagery (HSI). In this paper, a three-dimensional (3-D) nonlocal means filter is proposed for noise reduction of HSI. Recently, non-local means method attracts many attentions due to its global and local integrated property. Nonlocal algorithm searches the similar image patches in the whole scene to build the mean filter, so that it overcomes the disadvantage of local filter that only local pixels within a small neighbor is used, and the disadvantage of global filter that local structure is ignored. In order to explore the spectral-spatial correlation of HSI, nonlocal means method is extended from 2-D to 3-D. Furthermore, as HSI contains both of signal-independent and signal-dependent noises, variance-stabilizing transformation based on noise estimation is used to make noise reduction under the additive Gaussian noise model. Experiments with the real hyperspectral data set indicate that the proposed strategy can work well in both of detail preservation and noise removal.

Noise reduction is always an active research area in image processing due to its importance for the sequential tasks such as object classification and detection. In this paper, we develop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while noise component has not this property. The main contribution of the paper is in introducing nonlocal similarity and spectral-spatial structure of hyperspectral imagery into sparse representation. Non-locality means the self-similarity of image, by which the whole image can be partitioned into some groups containing similar patches. The similar patches in each group is sparsely represented with shared atoms making the signal and noise more easily separated. Sparse representation with spectral-spatial structure can exploit spectral and spatial joint correlations of hyperspectral imagery also making the signal and noise more distinguished, in which 3-D blocks are instead of 2-D patches for sparse coding. The experimental results indicate that the proposed method has a good quality of restoring the true signal from the noisy observation.

Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large number of feature variables exist but the amount of training samples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A linear sparse logistic regression model is proposed to combine feature selection and pixel classification into a regularized optimization problem with the constraint of sparsity. To explore the structured features, three-dimensional discrete wavelet transform (3D-DWT) is employed, which processes the hyperspectral data cube as a whole tensor instead of adapting the data to a vector or matrix. This allows more effective capturing of the spatial and spectral structure. The structure of the 3D-DWT features is imposed on the sparse model by group LASSO which selects the features on the group level. The advantages of our method are validated on the real hyperspectral data.