Real-Time Hyperspectral Image Compression Onto Embedded GPUs

Díaz, María Auxiliadora Martín; Guerra, Raúl; Horstrand, Pablo; Martel, Ernestina; López, Sebastián; Lopez, J.F.; Sarmiento, Roberto

doi:10.1109/jstars.2019.2917088

Cited by 31 publications

(52 citation statements)

References 23 publications

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“…Figure 7 shows the map of anomalous pixels obtained by the proposed method superimposed on a panchromatic representation of the scenes to be tested to ease the result interpretations. It should be mentioned that the results for CR = [12,16,20] are the same for all the 3 configuration settings. In displays shown in Figure 7, lines in blue color represent the n f frames employed to estimate the background distribution, green lines represent the hyperspectral frames, F i , free of anomalous pixels while red lines represent those frames identified by our proposal to be corrupted by anomalous pixels.…”

Section: Anomaly Detection Performancementioning

confidence: 89%

“…Considering that the anomaly detection process employs the p c vectors extracted in the compression stage, as it was explained in Section 2.2.3, simulations have been carried out using the same configuration settings employed to assess the compression process in Section 3.3.1 (BS = 1024, N bits = 12 and desired CR = [12,16,20]). With respect to the input parameters related with the anomaly detection process, α is set to 1% and n f to 100 hyperspectral frames, F i , for all test cases.…”

Section: Anomaly Detection Performancementioning

confidence: 99%

“…Fortunately, the emergence of computing boards that embed low energy consumption GPUs has made more attractive their use, especially in on-board applications carried out by unmanned aerial vehicles (UAVs) [9,17,18]. Nevertheless, these low-power GPUs (LPGPUs) are not as high performing as the same generation desktop GPUs [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of this set of core operations have already been tested using different algorithms specifically developed for unmixing applications [40], hyperspectral lossy compression [41] and anomaly detection [42,43]. Additionally, some of these algorithms have been successfully implemented into parallel computing devices, such as LPGPUs mounted on a UAV [20] and next-generation space-grade FPGAs [44], achieving real-time performance in both cases.…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection results using compressed-decompressed images for input parameters N bits = 12, BS = 1024 and CR =[12,16,20]. Lines in blue color represent the n f frames employed to estimate the background distribution.…”

mentioning

confidence: 99%

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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: Anomaly Detection Performancementioning

confidence: 89%

Section: Anomaly Detection Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2020

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An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

Dhenakaran

2021

Concurrency and Computation

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In this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.

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Burrows–Wheeler Transform for Enhancement of Lossless Document Image Compression Algorithms

Paikrao¹,

Doye²,

Bhalerao³

et al. 2023

Lecture Notes in Networks and Systems

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Real-Time Hyperspectral Image Compression Onto Embedded GPUs

Cited by 31 publications

References 23 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

An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

Burrows–Wheeler Transform for Enhancement of Lossless Document Image Compression Algorithms

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