Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery

Zhao, Chunhui; Deng, Weiwei; Yan, Yongde; Yao, Xin

doi:10.3390/s17081815

Cited by 18 publications

(11 citation statements)

References 24 publications

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“…Finally, we would like to highlight the existing trade-off between the causality in line-by-line approaches and how to model the background distribution, which is the most important part in any adopted solution for anomaly detection. Actually, very few publications are made in this field where the anomaly detection issue is addressed in a line-by-line fashion [43,47,59,60]. In the solution proposed in this manuscript, the background distribution is estimated from several of the first sensed hyperspectral image blocks, n f , under the assumption that they are free of anomalous signatures and hence, fully representative of the background distribution.…”

Section: Discussionmentioning

confidence: 99%

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

et al. 2020

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The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings further challenges for the remote-sensing research community due to the high data rate of the new-generation hyperspectral sensors and the limited amount of available on-board computational resources. This situation becomes even more stringent when different time-sensitive applications coexist, since different tasks must be sequentially processed onto the same computing device. In this work, we have dealt with this issue through the definition of a set of core operations that extracts spectral features useful for many hyperspectral analysis techniques, such as unmixing, compression and target/anomaly detection. Accordingly, it permits the concurrent execution of such techniques reusing operations and thereby requiring much less computational resources than if they were separately executed. In particular, in this manuscript we have verified the goodness of our proposal for the concurrent execution of both the lossy compression and anomaly detection processes in hyperspectral images. To evaluate the performance, several images taken by an unmanned aerial vehicle have been used. The obtained results clearly support the benefits of our proposal not only in terms of accuracy but also in terms of computational burden, achieving a reduction of roughly 50% fewer operations to be executed. Future research lines are focused on extending this methodology to other fields such as target detection, classification and dimensionality reduction.

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Section: Discussionmentioning

confidence: 99%

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

et al. 2020

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“…It should be emphasized that many new anomaly detection algorithms have emerged in recent years [53,54]. A separate survey is needed for this area alone.…”

Section: Residual Analysismentioning

confidence: 99%

“…As a result, only a small number of target signatures need to be converted from reflectance to radiance, and hence, a great reduction of computations has been achieved. Similarly, there are fast anomaly detectors based on random down sampling of background pixels [116], cluster centers of background pixels [16], progressive line scanning [53], and recursive implementation [117].…”

Section: Need To Improve Computational Efficiencymentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…For applications under strict real-time constraints in which the captured images must be processed in a short period of time, it is more efficient if the anomalies are uncovered as soon as the hyperspectral data are sensed. For this purpose, there have been recent solutions proposed to cope with this situation [28][29][30].…”

Section: Anomaly Detection Algorithmsmentioning

confidence: 99%

A Simulation Environment for Validation and Verification of Real Time Hyperspectral Processing Algorithms on-Board a UAV

et al. 2019

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The utilization of hyperspectral imaging sensors has gained a significant relevance among many different applications due to their capability for collecting a huge amount of information across the electromagnetic spectrum. These sensors have been traditionally mounted on-board satellites and airplanes in order to extract information from the Earth’s surface. Fortunately, the progressive miniaturization of these sensors during the last lustrum has enabled their use in other remote sensing platforms, such as drones equipped with hyperspectral cameras which bring advantages in terms of higher spatial resolution of the acquired images, more flexible revisit times and lower cost of the flight campaigns. However, when these drones are autonomously flying and taking real-time critical decisions from the information contained in the captured images, it is crucial that the whole process takes place in a safe and predictable manner. In order to deal with this problem, a simulation environment is presented in this work to analyze the virtual behavior of a drone equipped with a pushbroom hyperspectral camera used for assisting harvesting applications, which enables an exhaustive and realistic validation and verification of the drone real-time hyperspectral imaging system prior to its launch. To the best of the authors’ knowledge, the proposed environment represents the only solution in the state-of-the-art that allows the virtual verification of real-time hyperspectral image processing algorithms under realistic conditions.

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Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery

Cited by 18 publications

References 24 publications

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

A Simulation Environment for Validation and Verification of Real Time Hyperspectral Processing Algorithms on-Board a UAV

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