Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning

Lei, Jie; Fang, Shuo; Xie, Weiying; Li, Yunsong; Chang, Chein‐I

doi:10.1109/tgrs.2020.2982406

Cited by 48 publications

(13 citation statements)

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“…Using a hyperspectral camera [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], we can record scene radiance at high spectral and spatial resolution. This technique has been widely used in machine vision applications such as remote sensing [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], medical imaging [ 28 , 29 , 30 , 31 ], food processing [ 32 , 33 , 34 , 35 , 36 , 37 ], and anomaly detection [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ], as well as in the spectral characterization domain, including the calibration of color devices (e.g., cameras [ 45 ] and scanners [ 46 ]), scene relighting [ 47 , 48 ], and art conservation and archiving [ 49 , 50 , 51 ]. While useful, hyperspectral cameras are usually much more expensive than the RGB cameras.…”

Section: Introductionmentioning

confidence: 99%

On the Optimization of Regression-Based Spectral Reconstruction

Lin

Finlayson

2021

Sensors

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Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ2 and ℓ1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy.

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Section: Introductionmentioning

confidence: 99%

On the Optimization of Regression-Based Spectral Reconstruction

Lin

Finlayson

2021

Sensors

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“…Following the work of [ 28 ], for all single-layer methods, the number of latent features or clusters d is set as 5. This is because changing d in the range of [ 5 , 15 ] does not affect MF-based methods when d = 5 makes most of the methods perform well. For Offline NS-NMF, we set α = 0.8 and γ = 0.2; for Online NS-NMF, we set α = 0.8 and z = 20.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…For GraphSC, SR, and GRDSR, we turn graph regularization parameter α and sparse regularization parameter β from a set {10 −3 , 10 −2 , 10 −1 , 1, 10, 10 2 , 10 3 } and report the best result. Following the work of [28], for all single-layer methods, the number of latent features or clusters d is set as 5. is is because changing d in the range of [5,15] does not affect MF-based methods when d � 5 makes most of the methods perform well. For Offline NS-NMF, we set α � 0.8 and c � 0.2; for Online NS-NMF, we set α � 0.8 and z � 20.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Although these methods can achieve better detection results, their performances may be greatly affected by both the quality of feature representation methods and the stability of the clustering algorithms. To reduce the influence of clustering algorithms, reconstruction error-based methods have been proposed in which the error can be regarded as the outlier score for anomaly detection [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Lai

Jiang

et al. 2021

Computational Intelligence and Neuroscience

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Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.

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“…Meanwhile, it can also be formulated as a collection of spectral curves with discrimination ability, in which different curves correspond to different materials [2]. This spectral discrimination ability has enabled the wide application of HSIs, such as for environment monitoring [3] and military rescue [4], among other fields [5][6][7]. However, due to the limitation of imagery sensors, the number of electrons reaching a single band is limited, which makes the spatial resolution always sacrificed for the spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information

Zhao

Ning

et al. 2021

Remote Sensing

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Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.

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Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning

Cited by 48 publications

References 50 publications

On the Optimization of Regression-Based Spectral Reconstruction

On the Optimization of Regression-Based Spectral Reconstruction

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information

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