Hyperspectral anomaly detection: a performance comparison of existing techniques

Shah, Noman Raza; Maud, Abdur Rahman; Bhatti, Farrukh Aziz; Ali, Muhammad; Khurshid, Khurram; Maqsood, Moazam; Amin, Muhammad Bilal

doi:10.1080/17538947.2022.2146770

Cited by 16 publications

(9 citation statements)

References 128 publications

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“…(f) (g) As depicted in Figures 6 and 12, the GRX, LRX, and FRFR hardly detected anomalous aircraft for the San Diego-1 and Gulfport datasets, but on the contrary, the scattered The AUC values with bold font and red color in this table represent the optimal performance for each dataset. 2 The AUC values with bold font and blue color in this table represent the suboptimal performance for each dataset. As depicted in Figures 6 and 12, the GRX, LRX, and FRFR hardly detected anomalous aircraft for the San Diego-1 and Gulfport datasets, but on the contrary, the scattered background displays a notable degree score of anomalies.…”

Section: Detection Performancementioning

confidence: 99%

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A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

Zhao,

Yang,

Meng

et al. 2024

Remote Sensing

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With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

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Section: Detection Performancementioning

confidence: 99%

“…Hyperspectral imagery (HSI) contains abundant spatial and spectral information [1][2][3]. Hyperspectral remote sensors collect hyperspectral images by accreting two spatial dimensions of the image with an additional spectral dimension that comprises hundreds or thousands of approximately continuous spectral curves for land cover.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

Zhao,

Yang,

Meng

et al. 2024

Remote Sensing

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“…Since hyperspectral anomaly detection is a binary classification task that does not need prior knowledge about what is defined to be an anomaly, 4 traditional classification evaluation metrics can be utilized to evaluate anomaly detectors as long as their output is a binary image map. Some of the algorithms we used do not output binary images.…”

Section: Using Classification Metricsmentioning

confidence: 99%

“…In this treatment, anomaly detection is defined as a classification problem with two classes that do not require previous knowledge about the anomalies and aim to find the abnormalities in the hyperspectral image. 4 An anomaly detection algorithm subsequently classifies each pixel in the HS image into an anomaly class or a background class. The issue with some anomaly detection methods is that they make Gaussian assumptions about the hyperspectral data when the algorithm is developed.…”

Section: Introductionmentioning

confidence: 99%

“…5 A Gaussian assumption means that anomalies tend to be the small and less frequently occurring objects in the image. 4 If we have an image of the sea, and only one ship in the middle of the water, then the Gaussian assumption will work, and the ship would be considered the anomaly. However, real-life scenarios are much more complicated than this, which is why many anomaly detectors fail at identifying anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral unmixing-based anomaly detection

Younis

Hossain²,

Robinson

et al. 2023

Computational Imaging VII

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Research supporting improved anomaly detection performance benefits a wide range of technical applications. Thus, the definition of anomalies and the subsequent means to detect them are wide-ranging. This treatment presents an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing. Anomaly Detection is a binary classification that does not require prior information about the anomaly. For example, an anomaly detector applied to hyperspectral imaging (HSI) would take a hyperspectral image with hundreds of channels as an input and output a two-dimensional image map of pixel intensities based on a threshold procedure applied to the probability of that pixel being an anomaly. There have been many advancements in the field of HSI Anomaly Detection. Our ensemble method algorithm, presented here, addresses some of the shortcomings of current state-of-the-art techniques. We present details about the extracted end-members and use them for effective anomaly detection. Our current ensemble method opens the path for future machine-learning processes. We evaluated our method on multiple datasets and reported the F1-macro score. We suggest that the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve should not be used in Hyperspectral anomaly detection as an evaluation metric.

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Hyperspectral anomaly detection based on local contrast estimation and sub-block background estimation

Zhang,

Xu,

Yan

et al. 2023

Infrared Physics & Technology

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Hyperspectral anomaly detection: a performance comparison of existing techniques

Cited by 16 publications

References 128 publications

A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

Hyperspectral unmixing-based anomaly detection

Hyperspectral anomaly detection based on local contrast estimation and sub-block background estimation

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