On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach

Guerra, Raúl; Martel, Ernestina; Khan, Jehandad; López, Sebastián; Athanas, Peter; Sarmiento, Roberto

doi:10.1109/jstars.2017.2737958

Cited by 14 publications

(5 citation statements)

References 54 publications

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“…The shift-share analysis method usually takes the economic development status of the region where the city is located as a reference, uses the gross national product as an index to evaluate the economic aggregate, and studies the change process of the regional economic industrial structure through the dynamic development process [ 14 , 15 ]. When constructing the shift-share model, three indicators of the total economic volume of the case are generally selected: the first is the amount of change in the standardization industry department of the case study area based on the average growth rate of the region, that is, the deviation share W; the second is the impact of the structural difference between the case area and the regional economy, that is, the deviation of the industrial structure from the share R ; the third is the difference in the level of economic activity caused by the difference in economic growth between the various industrial sectors within the region and the corresponding industrial sector in the case, that is, the deviation of competitiveness from the share L .…”

Section: Optimization Of Urban Exhibition Tourism Developmentmentioning

confidence: 99%

Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing

Wan¹

2022

Computational Intelligence and Neuroscience

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MICE tourism has received the attention and support from relevant departments and tourism industry with the growth rate of recent years. In this paper, the dynamics mechanism of urban exhibition tourism development is studied by using high-performance computing, and the dynamics mechanism model of exhibition tourism is constructed. To improve the performance, the solution constructed in the adaptive joint algorithm is used as the initial solution, and then the initial solution is improved using the method proposed by the new neighborhood strategy. It promotes the development of MICE tourism and urban MICE tourism by combining research on theoretical aspects related to tourism development, such as the relationship between tourism development and exhibition, and the development dynamics and stages of MICE tourism. The research results show that, in the assessment of the development mechanism of urban MICE tourism, tourism resources, economic, and policy factors have a relatively large weight, among which the weight coefficient of tourism resources factor is 0.2411. It can be seen that the development power of MICE tourism should start from tourism resources, so that the development power mechanism of urban MICE tourism can be optimized and improved, which not only provides new methods and new ideas for the transformation and upgrading of tourism industry but also promotes tourism enterprises and related departments to creatively develop tourism resources and design competitive tourism products, so as to improve the supply quality of MICE tourism, and then continuously promote the process of supply-side reform of MICE tourism.

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Section: Optimization Of Urban Exhibition Tourism Developmentmentioning

confidence: 99%

Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing

Wan¹

2022

Computational Intelligence and Neuroscience

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“…The hardware architecture described in Section 4 has been implemented in OpenCL framework for the specification of Fast-ATGP [43]. The communication between CPUs and FPGAs in the full framework is completed using SDAccel, which can support standard OpenCL APIs.…”

Section: Experimental Environmentmentioning

confidence: 99%

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Lei

et al. 2019

Remote Sensing

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Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy. data analysis, it is rather rare and may be difficult to find because many of HSI-detected targets are relatively small and weak, such as anomalies [10]. Consequently, real-time target detection in HSI presents a great challenge and has become increasingly important.Currently, many algorithms have been developed for target detection in HSI, such as unsupervised fully constrained least squares (UFCLS) [11], automatic target-generation process based on an orthogonal subspace projector (ATGP-OSP) [12], and Reed-Xiaoli (RX) detector [13]. Among them, the performance of ATGP performs well in terms of detection accuracy and computational complexity according to the quantitative and comparative assessments [14,15]. However, despite the fact that automatic target detection has received increasing attention [16][17][18], two problems with real-time onboard applications need to be addressed. One is to continuously increase the scale of operations in the process of automatically iterating to update currently being found targets, which complicates the process of updating operator matrix. The other one is a large-scale matrix m...

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“…In Reference [19], a new linear unmixing algorithm that provided better accuracy and performance results than state-of-the-art alternatives was proposed. Optimized implementations of the algorithm were presented in Reference [25] for GPUs and Reference [26] for FPGAs. However, none of these implementations considers on-board processing.…”

Section: Unmixing Of Hyperspectral Imagesmentioning

confidence: 99%

A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images

et al. 2018

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Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo3 framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo3-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth.

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On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach

Cited by 14 publications

References 54 publications

Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing

Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images

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