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

Zhao, Chunhui; Li, Chuang; Feng, Shou; Jia, Xiuping

doi:10.1109/tgrs.2021.3128183

Cited by 14 publications

(5 citation statements)

References 51 publications

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“…In order to investigate the effects of different components on the detection performance [12,28,29], we attempt to construct a detector with dual spaces, which are the BKG space and the anomaly space. Thus, the distance between dual-space/dual-components is measured to investigate the influence of different components.…”

Section: The Ae-it For Different Anomalies By Correlating Multi-compo...mentioning

confidence: 99%

“…Similarly, Feng et al [11] combined local space and TV regularization to capture the local spatial structure information of the background. Zhao et al [12] introduced the enhanced total variation (ETV) term into the LRR model and developed an ETV with endmember background dictionary (ETVEBD) algorithm to enhance the spatial structure of HSI in the representation process. Candes et al [13] introduced the concept of decomposing the data matrix into low-rank and sparse components, denoted as the "low rank + sparse" matrix (X = L + S).…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Anomaly Detection with Auto-Encoder and Independent Target

Chen,

Li,

Yan

2023

Remote Sensing

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As an unsupervised data representation neural network, auto-encoder (AE) has shown great potential in denoising, dimensionality reduction, and data reconstruction. Many AE-based background (BKG) modeling methods have been developed for hyperspectral anomaly detection (HAD). However, their performance is subject to their unbiased reconstruction of BKG and target pixels. This article presents a rather different low rank and sparse matrix decomposition (LRaSMD) method based on AE, named auto-encoder and independent target (AE-IT), for hyperspectral anomaly detection. First, the encoder weight matrix, obtained by a designed AE network, is utilized to construct a projector for generating a low-rank component in the encoder subspace. By adaptively and reasonably determining the number of neurons in the latent layer, the designed AE-based method can promote the reconstruction of BKG. Second, to ensure independence and representativeness, the component in the encoder orthogonal subspace is made into a sphere and followed by finding of unsupervised targets to construct an anomaly space. In order to mitigate the influence of noise on anomaly detection, sparse cardinality (SC) constraint is enforced on the component in the anomaly space for obtaining the sparse anomaly component. Finally, anomaly detector is constructed by combining Mahalanobi distance and multi-components, which include encoder component and sparse anomaly component, to detect anomalies. The experimental results demonstrate that AE-IT performs competitively compared to the LRaSMD-based models and AE-based approaches.

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Section: The Ae-it For Different Anomalies By Correlating Multi-compo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection with Auto-Encoder and Independent Target

Chen,

Li,

Yan

2023

Remote Sensing

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“…Hence, the kernel-RX algorithm [15,16] was developed, which employs a kernel function and maps the data into a higher-dimensional feature space to characterize non-Gaussian distributions. Geometric-modeling-based methods [19][20][21][22][23][24][25][26][27][28] are another category of AD methods. Representation-based methods [19][20][21] have been successfully applied to AD because they do not need a specific distribution assumption, but they fail to capitalize on the high spectral correlation of HSIs.…”

Section: Introductionmentioning

confidence: 99%

“…An accurate background dictionary construction for LRR is still a challenging task. Linear mixing model (LMM)-based methods [29] have attracted considerable attention in the AD field due to their explicit physical descriptions obtained through background modeling and their ability to enhance the spatial structure [26][27][28]. Here, the background mixed pixels can be linearly represented by pure material signatures, which is normally accomplished by non-negative matrix factorization (NMF) [30], and they can be approximately written as the product of two non-negative matrices: an endmember matrix and an abundance matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Initially, Qu et al [26] applied spectral unmixing to model original HSI data and regarded the obtained abundance maps as the input for LRR. To enhance the HSI spatial structure smoothness, more recently, the authors of [28] proposed an enhanced total variation (ETV) model with an endmember background dictionary (EBD) by applying ETV to the row vectors of the representation coefficient matrix. The abovementioned methods both achieved promising performance with respect to background modeling using the LMM, though they were matrix-based methods that reshaped the 3D HSI into a 2D matrix and could not avoid destroying the spatial or spectral structure of the HSI.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Anomaly Detection Based on Regularized Background Abundance Tensor Decomposition

Shang

Jouni

et al. 2023

Remote Sensing

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The low spatial resolution of hyperspectral images means that existing mixed pixels rely heavily on spectral information, making it difficult to differentiate between the target of interest and the background. The endmember extraction method is powerful in enhancing spatial structure information for hyperspectral anomaly detection, whereas most approaches are based on matrix representation, which inevitably destroys the spatial or spectral information. In this paper, we treated the hyperspectral image as a third-order tensor and proposed a novel anomaly detection method based on a low-rank linear mixing model of the scene background. The obtained abundance maps possessed more distinctive features than the raw data, which was beneficial for identifying anomalies in the background. Specifically, the low-rank tensor background was approximated as the mode-3 product of an abundance tensor and endmember matrix. Due to the distinctive features of the background’s abundance maps, we characterized them by tensor regularization and imposed low rankness through CP decomposition, smoothness, and sparsity. In addition, we utilized the ℓ1,1,2-norm to characterize the tube-wise sparsity of the anomaly, since it accounted for a small portion of the scene. The experimental results obtained using five real datasets demonstrated the outstanding performance of our proposed method.

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