A reconfigurable multi-mode implementation of hyperspectral target detection algorithms

Boskovic, Dordije; Orlandić, Milica; Johansen, Tor Arne

doi:10.1016/j.micpro.2020.103258

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Some modules, such as the smile and keystone correction [55] and compression [13], modify the cube, as it propagates through the pipeline. Other modules, such as target detection [56] and classification, analyze the cube.…”

Section: Image Processing Pipelinementioning

confidence: 99%

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

2021

Self Cite

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Hyperspectral remote sensing reveals detailed information about the optical response of a scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both to enable more analysis on-board the imaging platform and to reduce downlink time. Here, the expected on-board performance of the SOM algorithm is calculated within two different satellite operational procedures: one in which the SOM is trained prior to imaging, and another in which the training is part of the operations. The two procedures are found to have advantages that are suitable to quite different situations. The computational requirements for SOMs of different sizes are benchmarked on the target hardware for the HYPSO-1 mission, and dimensionality reduction (DR) is tested as a way of reducing the SOM network size. We find that SOMs can run on the target on-board processing hardware, can be trained reasonably well using less than 0.1% of the total pixels in a scene, are accelerated by DR, and can achieve a relative quantization error of about 1% on scenes acquired by a previous hyperspectral imaging satellite, HICO. Moreover, if class labels are assigned to the nodes of the SOM, these networks can classify with a comparable accuracy to support vector machines, a common benchmark, on a few simple scenes.

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Section: Image Processing Pipelinementioning

confidence: 99%

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

2021

Self Cite

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“…Shimoda et al [9] proposed a sparse fully convolutional network based on FPGA for semantic segmentation, and adopted a fully pipelined architecture in hardware implementation [9]. Boskovic et al [10] implemented a multi-mode hyperspectral image target detection system based on FPGA [10], which could switch between three different target detection algorithms freely. However, these studies are based on the traditional von Neumann architecture, and a large amount of data needs to be transported from the underlying storage device to the memory of the host, which causes the problem of storage wall and power wall.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Remote-Sensing Data Processing Platform Based on Computable Storage

Qiu

Liu

Yang

et al. 2022

Mobile Information Systems

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Previous researches on accelerating remote sensing data processing are based on traditional von Neumann architecture, which separates storage and computation. Under the architecture, data must be obtained from the storage device first and then transmitted to Field Programmable Gate Array (FPGA) through the system bus. The power consumption caused by the data handling is huge, even exceeding the energy consumption required for data processing. In order to reduce the migration of remote sensing data and alleviate the problems of storage wall and power wall under von Neumann architecture, we design a remote sensing data processing platform based on the system architecture of computable storage, which uses Solid-State Disk (SSD) with computing capability to process the remote sensing data and realize accelerated remote sensing data processing. Based on this platform, applications related to remote sensing data processing such as compression, target detection, and image classification are deployed in SSD to improve the information acquisition rate in remote sensing data. Experimental results show that after compression being offloaded to SSD computing performance is improved by 2.27 times compared with the host CPU. Compared with the host GPU, the target detection speed is improved by 6.25% and the power consumption is reduced by 66.7%. Compared with the host, the detection speed of remote sensing image classification is improved by 78.8%, the power consumption is reduced by 70%, achieving the expected classification effect. The Remote Sensing data Processing Platform based on Computable Storage (CSRSPP) distributes various computing tasks to the SSD for execution, which not only improves the processing speed of computing tasks, but also greatly reduces the power consumption of the platform.

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Research on the Digital Measurement Method of Ship Flow Based on Computer Vision

Xia

2023

Lecture Notes on Data Engineering and Communications Technologies

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A reconfigurable multi-mode implementation of hyperspectral target detection algorithms

Cited by 9 publications

References 17 publications

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

Implementation of Remote-Sensing Data Processing Platform Based on Computable Storage

Research on the Digital Measurement Method of Ship Flow Based on Computer Vision

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