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Recently developed deep learning-based hyperspectral anomaly detection (HAD) methods typically include two steps where the deep feature extraction is not designed specifically for the HAD task. In this article, we propose a spectral distribution-aware estimation network (SDEN) that does not conduct feature extraction and anomaly detection in two separate steps but instead learns both jointly to estimate anomalies directly in an end-to-end manner without postprocessing. The unified framework can ensure that the extracted features serve better for anomaly detection. To preserve the distribution of hyperspectral images (HSIs) during dimensionality reduction, the SDEN introduces a spectral distribution (SD)-aware module imposed with a local-invariant constraint. More specifically, we adopt Markov chain Monte Carlo (MCMC) that enables the SD module to better estimate the distribution of the complex HSIs. Considering the powerful representation capability of Gaussian mixture model (GMM), the SDEN leverages it to establish an estimation module in the deep latent space where the anomaly resides in low density while the background not. We demonstrate that the SDEN yields competitive and highly promising results in comparison with the anomaly detection benchmarks. [ABSTRACT FROM AUTHOR]

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as water quality monitoring, crop stress surveying, and law enforcement-related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging because spatial coverage is very large and the background is highly heterogeneous. For pixel-based anomaly detection, computing cost in background modeling and a spatial-convolution-type detection process is very expensive. Thus, parallel and distributed computing is critical in reducing execution time, which can fit the need for real-time or near real-time detection from airborne and spaceborne platforms in support of immediate decision-making. This article reviews the recent advances in anomaly detection from hyperspectral remote sensing images and their implementation using parallel and distributed systems. The classical methods, i.e., the Reed–Xiaoli (RX) algorithm and its variants, including its real-time processing version, are illustrated in commodity graphic processing units (GPUs), cloud, and field-programmable gate array (FPGA) implementations. Practical issues and future development trends are also discussed. [ABSTRACT FROM AUTHOR]

In this article, we propose a weakly supervised low-rank representation (WSLRR) method for hyperspectral anomaly detection (HAD), which formulates deep learning-based HAD into a low-lank optimization problem not only characterizing the complex and diverse background in real HSIs but also obtaining relatively strong supervision information. Different from the existing unsupervised and supervised methods, we first model the background in a weakly supervised manner, which achieves better performance without prior information and is not restrained by richly correct annotation. Considering reconstruction biases introduced by the weakly supervised estimation, LRR is an effective method for further exploring the intricate background structures. Instead of directly applying the conventional LRR approaches, a dictionary-based LRR, including both observed training data and hidden learned data drawn by the background estimation model, is proposed. Finally, the derived low-rank part and sparse part and the result of the initial detection work together to achieve anomaly detection. Comparative analyses validate that the proposed WSLRR method presents superior detection performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) have unique advantages in distinguishing subtle spectral differences of different materials. However, due to complex and diverse backgrounds, unknown prior knowledge, and imbalanced samples, it is challenging to separate background and anomaly. In this article, we present a novel characterization of background-anomaly separability with a generative adversarial network (BASGAN) for hyperspectral anomaly detection. The key contribution is the proposal to explicitly constrain the background and anomaly separability by characterizing background spectral samples while avoiding anomaly reconstruction. First, we use a class saliency map extraction algorithm to obtain pseudobackground and anomaly samples for adversarial training. To further mitigate the suffering of anomaly contamination in background distribution estimation, we introduce background-anomaly separability constrained loss function to enhance the reconstruction of the background while weakening the anomaly reconstruction in a semisupervised way. Additionally, a discriminator is induced into the latent space to make the encoded representation resemble Gaussian distribution during adversarial training. The other is adversarial training in the reconstruction space so that the background estimation can be improved. Experiments conducted on real data sets illustrate the superior background-anomaly separability of the proposed method. [ABSTRACT FROM AUTHOR]

RGB image spectral super-resolution (SSR) is a challenging task due to its serious ill-posedness, which aims at recovering a hyperspectral image (HSI) from a corresponding RGB image. In this article, we propose a novel hybrid 2-D–3-D deep residual attentional network (HDRAN) with structure tensor constraints, which can take fully advantage of the spatial–spectral context information in the reconstruction progress. Previous works improve the SSR performance only through stacking more layers to catch local spatial correlation neglecting the differences and interdependences among features, especially band features; different from them, our novel method focuses on the context information utilization. First, the proposed HDRAN consists of a 2D-RAN following by a 3D-RAN, where the 2D-RAN mainly focuses on extracting abundant spatial features, whereas the 3D-RAN mainly simulates the interband correlations. Then, we introduce 2-D channel attention and 3-D band attention mechanisms into the 2D-RAN and 3D-RAN, respectively, to adaptively recalibrate channelwise and bandwise feature responses for enhancing context features. Besides, since structure tensor represents structure and spatial information, we apply structure tensor constraint to further reconstruct more accurate high-frequency details during the training process. Experimental results demonstrate that our proposed method achieves the state-of-the-art performance in terms of mean relative absolute error (MRAE) and root mean square error (RMSE) on both the “clean” and “real world” tracks in the NTIRE 2018 Spectral Reconstruction Challenge. As for competitive ranking metric MRAE, our method separately achieves a 16.06% and 2.90% relative reduction on two tracks over the first place. Furthermore, we investigate HDRAN on the other two HSI benchmarks noted as the CAVE and Harvard data sets, also demonstrating better results than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

On-board real-time anomaly detection has always been a challenging task in hyperspectral imaging analysis as it requires low computational complexity. Most of the existing anomaly detection algorithms inevitably trade off intensive computational complexity for high detection accuracy. This article presents a fast spectral–spatial anomaly detection algorithm with low complexity in hyperspectral images (HSIs) using morphological reconstruction and a simplified guided filter (Fast-MGD). Since the simple filtering techniques are applied, it is therefore feasible to achieve a field programmable gate array (FPGA)-based hardware implementation. More precisely, an effective deeply pipelined acceleration scheme is developed adopting high-level synthesis to support HSIs that are acquired over different scenes with different sizes and spectral bands. Experimental results show strong advantages of the proposed FPGA-based Fast-MGD in processing speed and resource consumption, while a high detection accuracy is remained. Its applicability in on-board real-time processing is demonstrated and verifie. [ABSTRACT FROM AUTHOR]

Recently, autoencoder (AE)-based anomaly detection has drawn considerable interest in hyperspectral image (HSI) analysis. In this article, we propose a novel discriminative reconstruction method for hyperspectral anomaly detection images with spectral learning (SLDR). The proposed algorithm has the following innovations. First, we use the spectral error map (SEM) to detect anomalies because the SEM can preferably reflect the spectral similarity of each pixel between the input and the reconstruction. Second, the loss function of the proposed SLDR model additionally introduces the spectral angle distance (SAD), which constrains the model to generate a reconstruction having greater spectral similarity to the input. Third, a constraint is imposed on the encoder, forcing it to generate latent variables that obey a unit Gaussian distribution, which helps the decoder to reconstruct a better background with respect to the input. Compared with the Reed–Xiaoli (RX), collaborative representation detection (CRD), attribute and edge-preserving filtering-based anomaly detection (AED) and adversarial autoencoder-based anomaly detection (AAE), through two real HSI data sets, the detection performance of the proposed SLDR method is found to be competitive. [ABSTRACT FROM AUTHOR]

Reliable detection of anomalies without any prior information is a critical yet challenging task in many applications, not least military and civilian fields. An intelligent anomaly detection system would use the material-specific spectral information in hyperspectral images (HSIs), thereby avoiding the loss of visually confusing objects. However, conventional hyperspectral anomaly detection methods are mainly achieved in an unsupervised way leading to limited performance due to lack of prior knowledge. In this article, we propose a novel autoencoder and adversarial-learning based semisupervised background estimation model (SBEM) that is trained only on the background spectral samples in order to accurately learn the background distribution. In particular, an unsupervised background searching method is firstly conducted on the original HSIs to search the background spectral samples. Our proposed SBEM consists of an encoder, a decoder, and a discriminator to thoroughly capture background distribution. Furthermore, jointly minimizing the reconstruction loss, spectral loss, and adversarial loss during training aids the model to learn the background distribution as required. Experiments on four real HSIs demonstrate that compared to the current state-of-the-art, the proposed framework yields higher detection capability and lower false alarm rate, which shows that it has a significant benefit in the tradeoff between detection accuracy and false alarm rate. [ABSTRACT FROM AUTHOR]

Limited by the anomalous spectral vectors in unlabeled hyperspectral images (HSIs), anomaly detection methods based on background distribution estimation often suffer from the contamination of anomalies, which decreases the estimation accuracy and, thus, weakens the detection performance. To address this problem, we proposed a novel semisupervised spectral learning (SSL) for the hyperspectral anomaly detection framework based on the generative adversarial network (GAN). GAN is applied and developed to estimate the background distribution in a semisupervised manner and obtain an initial spectral feature because of its strong representational capability and adversarial training advantage. In the proposed framework, an initial spatial feature is generated via morphological attribute filtering. Finally, an exponential constrained nonlinear suppression fusion technique is adopted to suppress the background and combine the complementary information in different features to obtain a fused detection map. The performance of the proposed anomaly detection technique is evaluated on a series of HSIs. Experimental results demonstrate that our method can outperform state-of-the-art anomaly detection methods. [ABSTRACT FROM AUTHOR]

The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it possible to capture anomalous samples [i.e., anomaly detection (AD)] that deviate from background samples. However, hyperspectral anomaly detection (HAD) faces various challenges due to high dimensionality, redundant information, and unlabeled and limited samples. To address these problems, this article proposes an unsupervised discriminative reconstruction constrained generative adversarial network for HAD (HADGAN). Our solution is mainly based on the assumption that the number of normal samples is much larger than the number of abnormal ones. The key contribution of this article is to learn a discriminative background reconstruction with anomaly targets being suppressed, which produces the initial detection image (i.e., the residual image between the original image and reconstructed image) with anomaly targets being highlighted and background samples being suppressed. To accomplish this goal, first, by using an autoencoder (AE) network and an adversarial latent discriminator, the latent feature layer learns normal background distribution and AE learns a background reconstruction as much as possible. Second, consistency enhanced representation and shrink constraints are added to the latent feature layer to ensure that anomaly samples are projected to similar positions as normal samples in the latent feature layer. Third, using an adversarial image feature corrector in the input space can guarantee the reliability of the generated samples. Finally, an energy-based spatial and distance-based spectral joint anomaly detector is applied in the residual map to generate the final detection map. Experiments conducted on several data sets over different scenes demonstrate its state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral differences between different materials, but in fact, it is difficult to distinguish between background and anomalies because the samples of anomalous pixels in HSIs are limited and susceptible to background and noise. To explore the discriminant features, a spectral adversarial feature learning (SAFL) architecture is specially designed for hyperspectral anomaly detection in this article. In addition to reconstruction loss, SAFL also introduces spectral constraint loss and adversarial loss in the network with batch normalization to extract the intrinsic spectral features in deep latent space. To further reduce the false alarm rate, we present an iterative optimization approach by a weighted suppression function that depends on the contribution rate of each feature to the detection. In particular, the structure tensor matrix is adopted to adaptively calculate the contribution rate of each feature. Benefiting from these improvements, the proposed method is superior to the typical and state-of-the-art methods either in detection probability or false alarm rate. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) can provide discriminative spectral signatures regarding the physical nature of different materials. It is this unique nature that makes HSIs to be of great interest in many fields. However, HSI application faces various challenges due to high dimensionality, redundant information, noisy bands, and insufficient samples. To address these problems, we propose an unsupervised band selection method based on deep latent spectral representation learning, called DLSRL, in this article. It imposes spectral consistency on deep latent space that resolves the issue of insufficient samples and spectral information lost in HSI interpretation. It pursues the low-dimensional optimal representation of the high-dimensional HSIs. In particular, an adaptive mapping relationship is constructed between the deep latent representation and the optimal subset to preserve physical significance optimally. Furthermore, a hierarchical optimization approach is introduced to achieve target detection with the selected subset. To verify the superiority of the proposed method, experiments have been conducted on four data sets captured by different sensors over different scenes. Comparative analyses validate that the proposed method presents superior performance in terms of high detection accuracy and low false alarm rate. [ABSTRACT FROM AUTHOR]

The high dimensionality of a hyperspectral image (HSI) provides the possibility of deeply capturing the underlying and intrinsic characteristics in spectra, such that targets embedded in the background can be detected. However, redundant information, deteriorated bands, and other interferences from background challenge the target detection problem. In this article, an effective feature extraction method based on unsupervised networks is proposed to mine intrinsic properties underlying HSIs. Our approach, called spectral regularized unsupervised networks (SRUN), imposes spectral regularization on autoencoder (AE) and variational AE (VAE) to emphasize spectral consistency, which is more suitable for characterizing spectral information of HSIs by hidden nodes than the original AE and VAE models. Then, we conduct a simple feature selection algorithm on the hidden nodes in the deepest code to select specific nodes that contain distinguishability between target and background, which is based on the spectral angular difference between a known target spectrum and spectra of other pixels in input. The selected nodes are further weighted adaptively to obtain a discriminative map depending on the observation that each selected node provides different contribution rates to target detection. Experimental results on several data sets illustrate that the proposed SRUN-based target detection algorithm is suitable for targets at the subpixel level and those with structural information. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection faces various levels of difficulty due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spectral–spatial information. In this paper, we address these problems and propose a novel approach, called spectral–spatial feature extraction (SSFE), which is based on two main aspects. In the spectral domain, we assume that the anomalous pixels are rarely present and all (or most) of the samples around the anomalies belong to background (BKG). Using this fact, we introduce a suppression function to construct a discriminative feature space and utilize a deep brief network to learn spectral representation and abstraction automatically that are used as inputs to the Mahalanobis distance (MD)-based detector. In the spatial domain, the anomalies appear as a small area grouped by pixels with high correlation among them compared to BKG. Therefore, the objects appearing as a small area are extracted based on attribute filtering, and a guided filter is further employed for local smoothness. More specifically, we extract spatial features of anomalies only from one single band obtained by fusing all bands in the visible wavelength range. Finally, we detect anomalies by jointly considering the spectral and spatial detection results. Several experiments are performed, which show that our proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) can describe the subtle differences in the spectral signatures of materials. However, they have low spatial resolution due to various hardware limitations. Improving it via postprocess without an auxiliary high-resolution (HR) image still remains a challenging problem. In this paper, we address this problem and propose a new HSI super-resolution (SR) method. Our approach, called deep feature matrix factorization (DFMF), blends feature matrix extracted by a deep neural network (DNN) with nonnegative matrix factorization strategy for super-resolving real-scene HSI. The estimation of the HR HSI is formulated as a combination of latent spatial feature matrix and spectral feature matrix. In the DFMF model, the input low-resolution (LR) HSI is first partitioned into several subsets according to the correlation matrix, and the key band is selected from each subset. Then, the key band group is super-resolved by a DNN model, and the HR key band group is then used as a guide to carry out deep spatial feature matrix. Specifically, the input LR HSI with prototype reflectance spectral vectors of the scene will be preserved when super-resolving in a spatial domain. Thus, the nonnegative spectral and spatial feature matrices are extracted simultaneously from alternately factorizing the pair of LR HSI and the HR key band group. Finally, the HR HSI is obtained by the integration of the spectral and spatial feature matrices. Experiments have been conducted on real-scene remote sensing HSI. Comparative analyses validate that the proposed DFMF method presents a superior super-resolving performance, as it preserves spectral information better. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly decreases the performance of postprocessing. In this letter, a deep stage convolutional neural network (CNN) with trainable nonlinearity functions is applied for the first time to remove noise in HSIs. Besides the fact that the weight and bias matrices are learned from cubic training clean-noisy HSI patches, the nonlinearity functions in each stage are also trainable, which differ from the conventional CNN with a fixed nonlinearity function. Compared with the state-of-the-art HSI denoising methods, the experimental results on both synthetic and real HSIs confirm that the proposed method can obtain a more effective and efficient performance. [ABSTRACT FROM PUBLISHER]

A hyperspectral image (HSI) super-resolution (SR) is a highly attractive topic in computer vision. However, most existed methods require an auxiliary high-resolution (HR) image with respect to the input low-resolution (LR) HSI. This limits the practicability of these HSI SR methods. Moreover, these methods often destroy the important spectral information. This letter presents a deep spectral difference convolutional neural network (SDCNN) with the combination of a spatial-error-correction (SEC) model for HSI SR. This method allows for full exploration of the spectral and spatial correlations, which achieves a good spatial information enhancement and spectral information preservation. In the proposed method, the key band is automatically selected and super-resolved with the boundary bands. Meanwhile, spectral difference mapping between the LR and HR HSIs can be learned by the SDCNN, and then be transformed according to the SEC model, which aims at correcting the spatial error while preserving the spectral information. The rest nonkey bands will be super-resolved under the guidance of the transformed spectral difference. Experimental results on synthesized and real-scenario HSIs suggest that the proposed method: 1) achieves comparable performance without requiring any auxiliary images of the same scene and 2) requires less computation time than the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]