Advancing Algorithmic Adaptability in Hyperspectral Anomaly Detection with Stacking-Based Ensemble Learning

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD.

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

Hyperspectral anomaly detection (HAD) aims to separate subtle anomalies of a given hyperspectral image (HSI) from its background, which is a hot topic as well as a challenging inverse problem. Despite the significant success of the deep learning-based HAD methods, they are hard to interpret due to their black-box nature. Meanwhile, deep learning methods suffer from the identity mapping (IM) problem, referring to the network excessively focusing on the precise reconstruction of the background while neglecting the appropriate representation of anomalies. To this end, this paper proposes a self-attention-aided deep unfolding tensor representation network (SATRNet) for interpretable HAD by solving the tensor representation (TR)-based optimization model within the framework of deep networks. In particular, a Self-Attention Learning Module (SALM) was first designed to extract discriminative features of the input HSI. The HAD problem was then formulated as a tensor representation problem by exploring both the low-rankness of the background and the sparsity of the anomaly. A Weight Learning Module (WLM) exploring local details was also generated for precise background reconstruction. Finally, a deep network was built to solve the TR-based problem through unfolding and parameterizing the iterative optimization algorithm. The proposed SATRNet prevents the network from learning meaningless mappings, making the network interpretable to some extent while essentially solving the IM problem. The effectiveness of the proposed SATRNet is validated on 11 benchmark HSI datasets. Notably, the performance of SATRNet against adversarial attacks is also investigated in the experimentation, which is the first work exploring adversarial robustness in HAD to the best of our knowledge.

Lately, generative adversarial networks (GAN)-based methods have drawn extensive attention and achieved a promising performance in the field of hyperspectral anomaly detection (HAD) owing to GAN’s powerful data generation capability. However, without considering the background spatial features, most of these methods can not obtain a GAN with a strong background generation ability. Besides, they fail to address the hyperspectral image (HSI) redundant information disturbance problem in the anomaly detection part. To solve these issues, the unsupervised generative adversarial network with background spatial feature enhancement and irredundant pooling (BEGAIP) is proposed for HAD. To make better use of features, spatial and spectral features union extraction idea is also applied to the proposed model. To be specific, in spatial branch, a new background spatial feature enhancement way is proposed to get a data set containing relatively pure background information to train GAN and reconstruct a more vivid background image. In a spectral branch, irredundant pooling (IP) is invented to remove redundant information, which can also enhance the background spectral feature. Finally, the features obtained from the spectral and spatial branch are combined for HAD. The experimental results conducted on several HSI data sets display that the model proposed acquire a better performance than other relevant algorithms.

The general objective of anomaly detection (AD) in hyperspectral imagery is to detect full-pixel targets. To meet this purpose, the global AD methods can achieve more reliable results than the local methods in terms of time and accuracy. The kernel-based Support Vector Data Description (SVDD) has recently received great attention in the hyperspectral AD applications. This paper presents a global SVDD-based method for autonomous full-pixel AD. The method consists of three steps: clustering, background modeling, and autonomous AD. Experimental results on a hyperspectral dataset show the superiority of the proposed method comparing to the global-based SVDD method.

Hyperspectral anomaly detection (HAD) is a challenging task since it identifies the anomaly targets without prior knowledge. In recent years, deep learning methods have emerged as one of the most popular algorithms in the HAD. These methods operate on the assumption that the background is well reconstructed while anomalies cannot, and the degree of anomaly for each pixel is represented by reconstruction errors. However, most approaches treat all background pixels of a hyperspectral image (HSI) as one type of ground object. This assumption does not always hold in practical scenes, making it difficult to distinguish between backgrounds and anomalies effectively. To address this issue, a novel deep feature aggregation network (DFAN) is proposed in this paper, and it develops a new paradigm for HAD to represent multiple patterns of backgrounds. The DFAN adopts an adaptive aggregation model, which combines the orthogonal spectral attention module with the background-anomaly category statistics module. This allows effective utilization of spectral and spatial information to capture the distribution of the background and anomaly. To optimize the proposed DFAN better, a novel multiple aggregation separation loss is designed, and it is based on the intra-similarity and inter-difference from the background and anomaly. The constraint function reduces the potential anomaly representation and strengthens the potential background representation. Additionally, the extensive experiments on the six real hyperspectral datasets demonstrate that the proposed DFAN achieves superior performance for HAD. The code is available at https://github.com/ChengXi-1217/DFAN-HAD.

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

Anomaly detection in hyperspectral images involves identifying deviations or outliers within the high-dimensional spectral data captured across numerous contiguous wavelength bands. Hyperspectral imaging provides detailed spectral information, making it a powerful tool for detecting subtle variations in materials or objects that are not visible in traditional imaging techniques. The proposed system employs advanced machine learning techniques, including convolutional neural networks (CNNs) and autoencoders, to analyse hyperspectral images for anomalies. By training the models on a dataset of normal hyperspectral images, the system learns the inherent spectral characteristics and identifies patterns of typical data. New hyperspectral data is then analysed to detect deviations that may indicate potential anomalies. This approach is particularly effective in applications such as remote sensing, environmental monitoring, precision agriculture, mineral exploration, and quality control, where detecting anomalies like land degradation, crop stress, or material defects is crucial.

Anomaly detection is a fundamental task in hyperspectral image (HSI) processing. However, most existing methods rely on pixel feature vectors and overlook the relational structure information between pixels, limiting the detection performance. In this article, we propose a novel approach to hyperspectral anomaly detection that characterizes the HSI data using a vertex- and edge-weighted graph with the pixels as vertices. The constructed graph encodes rich structural information in an affinity matrix. A crucial innovation of our method is the ability to obtain internal relations between pixels at multiple topological scales by processing different powers of the affinity matrix. This power processing is viewed as a graph evolution, which enables anomaly detection using vertex extraction formulated as a quadratic programming problem on graphs of varying topological scales. We also design a hierarchical guided filtering architecture to fuse multiscale detection results derived from graph evolution, which significantly reduces the false alarm rate. Our approach effectively characterizes the topological properties of HSIs, leveraging the structural information between pixels to improve anomaly detection accuracy. Experimental results on four real HSIs demonstrate the superior detection performance of our proposed approach compared to some state-of-the-art hyperspectral anomaly detection methods.

Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the use of different datasets in previous studies, an extensive performance comparison of these algorithms is missing. In this paper, an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories. In addition, this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets. Results indicate that attribute and edge-preserving filtering-based detection (AED), local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW) and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD) perform better as indicated by the mean and median values of area under the receiver operating characteristic (ROC) curves. Finally, this paper studies the effect of various dimensionality reduction techniques on anomaly detection. Results indicate that reducing the number of components to around 20 improves the performance; however, any further decrease deteriorates the performance.

In this study, a hyperspectral anomaly detection method based on Laplacian matrix (HADLAP) is proposed. This paper addresses the problem of determining covariance matrix inversion in high-dimensional data and proposes a new approach for identifying anomalies in hyperspectral images (HSIs). The study’s goals are to find anomalous locations in HSIs and to deal with the problem of calculating the inversion of the covariance matrix of high dimensional data. The method is centered on two main concepts. One of them is decomposition process. The other one is detection process. First, HSI data is decomposed as a low rank and sparse matrices. Second, the sparse component of the data is used to build Mahalanobis Distance (MD). In this study, go decomposition (GoDec) algorithm is employed to decompose the data. Then, the distance is calculated by obtained matrix with aim of detection of anomalous pixels in the HSIs. The method differs from previous studies that covariance matrix in the distance is computed with Laplacian matrix and MD. Experiments conducted on three hyperspectral datasets present the superiority and effectiveness of the proposed framework in terms of detection performance with respect to state-of-the-art methods.

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial-spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation learning (ETLR) model is proposed for HAD. Specifically, the original 3-D hyperspectral image (HSI) data is firstly decomposed into a structural background component, an anomaly component and a noise component. Among them, with the help of multi-subspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1,1</sub> -norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior than current HAD algorithms.

Hyperspectral anomaly detection is one of the most active topics in hyperspectral image (HSI) analysis. The fine spectral information of HSIs allows us to uncover anomalies with very high accuracy. Recently, an intrinsic image decomposition (IID) model has been introduced for low-rank IID (LRIID) in multispectral images. Inspired by the LRIID, which is able to effectively recover the reflectance and shading components of the multispectral image, this article adapts the LRIID for obtaining the reflectance component of HSIs (which is the key feature for the discrimination of different objects). In order to exploit the reflectance component, we also propose a new dual window density (DWD)-based detector for anomaly detection, which is based on the idea that anomalies are usually rare pixels and, thus, exhibit low density in the image. The density analysis of DWD is intended not only to circumvent the Gaussian assumption regarding the distribution of HSI data, but also to mitigate the contamination of background statistics caused by anomalies. The dual window operation of our DWD is specifically designed to adaptively calculate the density of each pixel under test, so as to identify anomalies with nonspecific sizes. Our experimental results, obtained on a database of real HSIs including Airport, Beach, and Urban scenes, demonstrate the superiority of the proposed method in terms of detection performance when compared to other widely used anomaly detection methods.

Over the last two decades, anomaly detection (AD) has been known to play a critical role in hyperspectral image analysis, which provides a new way to distinguish the targets from the background without prior knowledge. Recently, the representation-based methods were proposed and soon became a significant type of methods on hyperspectral AD. In this paper, a novel AD algorithm based on convolutional neural network (CNN) and low-rank representation (LRR) is proposed. First, a CNN model is built and trained on hyperspectral image (HSI) datasets to accurately obtain the resulting abundance maps. Compared with the raw dataset, abundance maps contain more distinctive features to identify anomalies from the background. Second, a dictionary is constructed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm to stably represent the background component. Third, a matrix decomposition method based on LRR is adopted. In this way, a coefficient matrix corresponding to the constructed dictionary is obtained, which is low rank. At the same time, a residual matrix can be obtained as well, which is sparse. Finally, anomalies can be extracted from the residual matrix. The experimental results show that the proposed method achieves a superior performance compared to some of the state-of-the-art methods in the field of hyperspectral AD.

Deep learning has achieved excellent performance in the field of computer vision and gained attention in the field of hyperspectral anomaly detection (HAD). Convolutional neural networks (CNN) based on pixel pairing features have achieved a certain detection effect, which is limited by background complexity in practical applications. This study proposes an adaptive weighted HAD algorithm that combines an autoencoder (AE) and CNN. Labelled hyperspectral images (HSIs) are used to train a CNN as a binary classifier for similarity discrimination in the data preparation stage, where morphological attribute filtering (MAF) is performed on images in the spatial dimension of the HSIs to highlight anomaly. The spectral angle is used as a measure of reconstruction error to train the AE, and the reconstruction error obtained using the AE is used as an adaptive weight to calculate the anomaly score, which considerably eliminates the blurring of the boundary between the background and anomalies. Through comparisons with some state-of-the-art methods, the effectiveness of the proposed method in improving detection accuracy and increasing background and anomaly discrimination is verified on three real datasets.