Background Dictionary Research Articles

Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction; and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method.

Hyperspectral anomaly detection based on weighted low-rank sparse dictionary learning

The geometric model-based hyperspectral anomaly detection techniques have garnered a lot of interest recently. These methods are predicated on the assumption that the background can be represented by a dictionary of spectral vectors, but the anomaly cannot. Building an objective model with high representational capabilities and obtaining precise background modeling are therefore key aspects of this type of method. The background dictionary is also readily tainted by anomalies due to the limitations of the current approaches, making the detection findings less reliable. To address the above problems, this paper proposes a weighted low-rank sparse dictionary learning method (WLSDL). This model organically combines sparse representation with low-rank representation in order to effectively depict the intricate background distribution. The weights relating to the eigenvalues of the image are designed by mining the physical characteristics of the HSI. This can enhance the nuclear norm’s capacity for representation, resulting in a background dictionary with more representativeness. Furthermore, the capped norm constraint is incorporated in the objective function to decrease the effect of anomalies and noises on background modeling, hence minimizing anomaly pollution on the background dictionary. The residual image effectively increases the accuracy of anomaly identification since it makes it easier to distinguish between anomalies and background. We use three hyperspectral datasets to evaluate the proposed method. The experimental findings demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods.

Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary

In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method.

Robust detection and refinement of saliency identification

Salient object detection is an increasingly popular topic in the computer vision field, particularly for images with complex backgrounds and diverse object parts. Background information is an essential factor in detecting salient objects. This paper suggests a robust and effective methodology for salient object detection. This method involves two main stages. The first stage is to produce a saliency detection map based on the dense and sparse reconstruction of image regions using a refined background dictionary. The refined background dictionary uses a boundary conductivity measurement to exclude salient object regions near the image's boundary from a background dictionary. In the second stage, the CascadePSP network is integrated to refine and correct the local boundaries of the saliency mask to highlight saliency objects more uniformly. Using six evaluation indexes, experimental outcomes conducted on three datasets show that the proposed approach performs effectively compared to the state-of-the-art methods in salient object detection, particularly in identifying the challenging salient objects located near the image's boundary. These results demonstrate the potential of the proposed framework for various computer vision applications.

Hyperspectral anomaly detection based on adaptive background dictionary construction and collaborative representation

ABSTRACT Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size.

Pixel-associated autoencoder for hyperspectral anomaly detection

Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive efficacy. However, the current methodologies often neglect the global pixel similarity of the hyperspectral image (HsI), thereby limiting reconstruction accuracy. This study introduces an innovative pixel-associated AE approach that leverages pixel associations to augment hyperspectral anomaly detection. First, a dictionary construction methodology was introduced based on superpixel distance estimation to construct distinct dictionaries for background and local anomalies. Second, to recognize pixel similarities, the similarity metric of each pixel from the original HsI to the background dictionary and to the local anomaly dictionary was employed as the AE network input in lieu of the original HsI. Third, a dual hidden-layer feature similarity constraint network was proposed to enhance the reconstruction error of background and anomaly targets. Finally, the reconstruction error was utilized to score the anomaly target. The proposed method was benchmarked against other state-of-the-art techniques using synthetic and real HsI datasets to assess its effectiveness. The experimental results demonstrated the superior performance of the proposed method, outperforming the alternatives.

Hyperspectral Anomaly Detection With Tensor Average Rank and Piecewise Smoothness Constraints.

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial-spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the l2.1 norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.

Hyperspectral Anomaly Detection Using Spatial–Spectral-Based Union Dictionary and Improved Saliency Weight

Hyperspectral anomaly detection (HAD), which is widely used in military and civilian fields, aims to detect the pixels with large spectral deviation from the background. Recently, collaborative representation using union dictionary (CRUD) was proved to be effective for achieving HAD. However, the existing CRUD detectors generally only use the spatial or spectral information to construct the union dictionary (UD), which possibly causes a suboptimal performance and may be hard to use in actual scenarios. Additionally, the anomalies are treated as salient relative to the background in a hyperspectral image (HSI). In this article, a HAD method using spatial–spectral-based UD and improved saliency weight (SSUD-ISW) is proposed. To construct robust UD for each testing pixel, a spatial-based detector, a spectral-based detector and superpixel segmentation are jointly considered to yield the background set and anomaly set, which provides pure and representative pixels to form a robust UD. Differently from the conventional operation that uses the dual windows to construct the background dictionary in the local region and employs the RX detector to construct the anomaly dictionary in a global scope, we developed a robust UD construction strategy in a nonglobal range by sifting the pixels closest to the testing pixel from the background set and anomaly set to form the UD. With a preconstructed UD, a CRUD is performed, and the product of the anomaly dictionary and corresponding representation coefficient is explored to yield the response map. Moreover, an improved saliency weight is proposed to fully mine the saliency characteristic of the anomalies. To further improve the performance, the response map and saliency weight are combined with a nonlinear fusion strategy. Extensive experiments performed on five datasets (i.e., Salinas, Texas Coast, Gainesville, San Diego and SpecTIR datasets) demonstrate that the proposed SSUD-ISW detector achieves the satisfactory AUCdf values (i.e., 0.9988, 0.9986, 0.9939, 0.9945 and 0.9997), as compared to the comparative detectors whose best AUCdf values are 0.9938, 0.9956, 0.9833, 0.9919 and 0.9991.

CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate. However, the traditional CR presents a low utilization rate for deep latent features in hyperspectral images, making the dictionary construction and the optimization of weight matrix sub-optimal. Due to the excellent capacity of neural networks for generation, we formulate the deep learning-based method into CR optimization in both global and local streams, and propose a novel hyperspectral anomaly detection method based on collaborative representation neural networks (CRNN) in this paper. In order to gain a complete background dictionary and avoid the pollution of anomalies, the global dictionary is collected in the global stream by optimizing the dictionary atom loss, while the local background dictionary is obtained by using a sliding dual window. Based on the two dictionaries, our two-stream networks are trained to learn the global and local representation of hyperspectral data by optimizing the objective function of CR. The detection result is calculated by the fusion of residual maps of original and represented data in the two streams. In addition, an autoencoder is introduced to obtain the hidden feature considered as the dense expression of the original hyperspectral image, and a feature extraction network is concerned to further learn the comprehensive features. Compared with the shallow learning CR, the proposed CRNN learns the dictionary and the representation weight matrix in neural networks to increase the detection performance, and the fixed network parameters instead of the complex matrix operations in traditional CR bring a high inference efficiency. The experiments on six public hyperspectral datasets prove that our proposed CRNN presents the state-of-the-art performance.

Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation

The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized.

Flexible Hyperspectral Anomaly Detection Using Weighted Nuclear Norm

It has been demonstrated that nuclear-norm-based low-rank representation is capable of modeling cluttered backgrounds in hyperspectral images (HSIs) for robust anomaly detection. However, minimizing the nuclear norm regularizes each singular value equally during rank reduction, which restricts the capacity and flexibility of modeling the major structures of the background. To address this problem, we propose detection of anomaly pixels in HSIs using the weighted nuclear norm, which can preserve the major singular values during rank reduction. We present a down-up sampling scheme to remove plausible anomaly pixels from the image as much as possible and learn a robust principal component analysis (PCA) background dictionary. From a dictionary, we develop a weighted nuclear-norm minimization model to represent the background with a low-rank coefficients matrix that can be effectively optimized using the standard alternating direction method of multipliers (ADMM). Due to the flexible modeling capacity using the weighted nuclear norm, anomaly pixels can be distinguished from the background with the reconstruction error. The experimental results on two real HSIs datasets demonstrate the effectiveness of the proposed method for anomaly detection.

Adaptively Dictionary Construction for Hyperspectral Target Detection

The task of hyperspectral images (HSIs) target detection is to identify whether the target spectral sequences present in the HSI. Recently, the topic of representation models has received much interest in hyperspectral target detection. The performance of representation models depends on whether the corresponding dictionary and sparse matrix can correctly recover the original spectrum. Therefore, the background dictionary of these models should contain the spectra of all classes except the target spectrum, i.e., the dictionary should be over-complete. However, most representation models cannot satisfy this condition. Moreover, due to the potentially large spectral similarity between the target and the background, representation models perform poorly in background suppression. Aiming to solve these issues, a novel adaptively dictionary constructing (ADC) strategy with background suppression sparse representation (BSSR) module is proposed in this letter, called adaptively dictionary construction for target detection (ADCTD). Specifically, the proposed ADC is adopted to segment the HSI into superpixels consisting of pixels with similar spectrums. This process can be considered as an unsupervised coarse classification process, which can construct an over-complete background dictionary. In addition, the BSSR is adopted to improve the separation of the target and background by a linear function. Experiments on three datasets demonstrate the superiority of the proposed ADCTD.

Anomaly Detection of Hyperspectral Image With Hierarchical Antinoise Mutual-Incoherence- Induced Low-Rank Representation

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually difficult to construct an accurate background dictionary for the background pixels composed of different land-covers, and completely separate sparse anomaly targets from various complicated background pixels with complex mixed noise interference. To address these challenges, we propose an anti-noise hierarchical mutual-incoherence-induced discriminative learning (AHMID) method for AD of HSI. A structural incoherence constraint is designed to constrain the inherent dissimilarity and incoherence between background and anomalies for improving their separability. Then, a first-order statistic constraint is conducted on targets to enhance the anomaly representation, and a decentralization constraint is used on background to suppress the background representation. Meanwhile, a mixed noise model is constructed by ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</sub> -norm and Frobenius norm to improve the anti-noise performance. Finally, a hierarchical alternating strategy is developed to gradually optimize the background and anomalies. Experiments on six HSI AD datasets show that the proposed method outperforms a few state-of-the-art AD algorithms. Code: https://github.com/HalongL/HAD-AHMID.

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI). Nevertheless, the existing methods for achieving hyperspectral anomaly detection (HAD) generally only consider one of them, failing to comprehensively exploit them to further promote the detection performance. To address the issue, a novel HAD method, which integrates both SR and CR, is proposed in this article. To be specific, an SR model, whose overcomplete dictionary is generated by means of the density-based clustering algorithm and superpixel segmentation method, is first constructed for each pixel in an HSI. Then, for each pixel in an HSI, the used atoms in SR model are sifted to form the background dictionary corresponding to the CR model. To fully exploit both SR and CR information, we further combine the residual features obtained from both SR and CR model by the nonlinear transformation function to generate the response map. Finally, to preserve contour information of the objects, a postprocessing operation with guided filter is imposed into the response map to acquire the detection result. Experiments conducted on simulated and real datasets demonstrate that the proposed SRCR outperforms the state-of-the-art methods.

Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection.

Recently, low-rank representation (LRR) methods have been widely applied for hyperspectral anomaly detection, due to their potentials in separating the backgrounds and anomalies. However, existing LRR models generally convert 3-D hyperspectral images (HSIs) into 2-D matrices, inevitably leading to the destruction of intrinsic 3-D structure properties in HSIs. To this end, we propose a novel tensor low-rank and sparse representation (TLRSR) method for hyperspectral anomaly detection. A 3-D TLR model is expanded to separate the LR background part represented by a tensorial background dictionary and corresponding coefficients. This representation characterizes the multiple subspace property of the complex LR background. Based on the weighted tensor nuclear norm and the LF,1 sparse norm, a dictionary is designed to make its atoms more relevant to the background. Moreover, a principal component analysis (PCA) method can be assigned as one preprocessing step to exact a subset of HSI bands, retaining enough the HSI object information and reducing computational time of the postprocessing tensorial operations. The proposed model is efficiently solved by the well-designed alternating direction method of multipliers (ADMMs). A comparison with the existing algorithms via experiments establishes the competitiveness of the proposed method with the state-of-the-art competitors in the hyperspectral anomaly detection task.

Selective Search Collaborative Representation for Hyperspectral Anomaly Detection

As an important tool in hyperspectral anomaly detection, collaborative representation detection (CRD) has attracted significant attention in recent years. However, the lack of global feature utilization, the contamination of the background dictionary, and the dependence on the sizes of the dual-window lead to instability of anomaly detection performance of CRD, making it difficult to apply in practice. To address these issues, a selective search collaborative representation detector is proposed. The selective search is based on global information and spectral similarity to realize the flexible fusion of adjacent homogeneous pixels. According to the homogeneous segmentation, the pixels with low background probability can be removed from the local background dictionary in CRD to achieve the purification of the local background and the improvement of detection performance, even under inappropriate dual-window sizes. Three real hyperspectral images are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves, and area under curve values, as well as by running time. Comparison with CRD proves that the proposed method can effectively improve the anomaly detection accuracy of CRD and reduce the dependence of anomaly detection performance on the sizes of the dual-window.

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

Meta-Pixel-Driven Embeddable Discriminative Target and Background Dictionary Pair Learning for Hyperspectral Target Detection

In hyperspectral target detection, the spectral high-dimensionality, variability, and heterogeneity will pose great challenges to the accurate characterizations of the target and background. To alleviate the problems, we propose a Meta-pixel-driven Embeddable Discriminative target and background Dictionary Pair (MEDDP) learning model by combining low-dimensional embeddable subspace projection and the discriminative target and background dictionary pair learning. In MEDDP, the meta-pixel set is built by taking the merits of homogeneous superpixel segmentation and the local manifold affinity structures, which can significantly reduce the influence of spectral variability and find the most typical and informative prototype spectral signature. Afterward, an embeddable discriminative dictionary pair learning model is established to learn a target and background dictionary pair based on the structural incoherent constraint with embeddable subspace projection. The proposed joint learning strategy can reduce the high-dimensional redundant information and simultaneously enhance the discrimination and compactness of the target and background dictionaries. The proposed MEDDP model is solved by an iterative and alternate optimization algorithm and applied with the meta-pixel-level target detection method. Experimental results on four benchmark HSI datasets indicate that the proposed method can consistently yield promising performance in comparison with some state-of-the-art target detectors.

Reweighted Nuclear Norm and Total Variation Regularization With Sparse Dictionary Construction for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is an important technique in the field of remote sensing image processing. Over the last few years, low rank and sparse matrix decomposition (LRSMD) has played an increasingly significant role in hyperspectral anomaly detection. The detection performance of the LRSMD-based anomaly detectors is primarily determined by prior constraints and the background dictionary construction method. To increase the detection accuracy, we proposed the reWeighted Nuclear Norm and total variation regularization with Sparse Dictionary construction for hyperspectral Anomaly Detection (WNNSDAD), which incorporated reweighted nuclear norm and total variation regularizations as the prior constraints into the LRSMD model, and constructed a sparse background dictionary without the need of clustering. Compared to the standard nuclear norm, the reweighted nuclear norm helped to overcome the challenge of an unbalanced penalty for a singular value and ensure a more effective low rank approximation. Simultaneously, total variation regularization was introduced as a piecewise smoothing constraint, which helped to maintain the spatial correlation of the hyperspectral image. Additionally, we proposed a background dictionary construction method, by which a relatively complete background dictionary could be obtained without clustering, and the background part could be represented more reliably. The experiments on seven real-world hyperspectral datasets show that in comparison to eight state-of-the-art anomaly detection methods, the proposed WNNSDAD method demonstrated greater accuracy.

Enhanced Total Variation Regularized Representation Model With Endmember Background Dictionary for Hyperspectral Anomaly Detection

In recent years, several representation models based on total variation (TV) have been proposed for hyperspectral imagery (HSI) anomaly detection. However, the TV terms of these works are directly imposed on the representation coefficient matrix, which can destroy the spatial structure of an HSI to some extent. Besides, as the spatial resolution of an HSI is relatively low, mixed pixels existing in an HSI can lead to anomaly component contamination, which can make the difference between background and anomalies not significant enough. To address these issues, a novel enhanced TV (ETV) with an endmember background dictionary (EBD) for hyperspectral anomaly detection is proposed. The ETV is designed to be used on the row vectors of the representation coefficient matrix to enhance the spatial structure of an HSI in the presentation process. Furthermore, the proposed ETV regularized representation model with EBD (ETVEBD) method elaborates on a background dictionary constructed by endmembers of background pixels, which are pure spectral signatures of background pixels. The proposed EBD can decrease the influence of anomaly components in mixed pixels, and the coefficient matrix of the EBD has more physical meanings. The proposed method is evaluated on four hyperspectral datasets, and the experiment results show that its performance is the best compared with the other seven state-of-the-art methods.