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<p>Hyperspectral sensors capture a wide range of spectral data, making them crucial for Earth observation applications, but this fact poses significant challenges for embedded systems with limited resources. Nevertheless, most studies only perform one application at the same time, so multi-applications in the same device are not considered since high-performance and low hardware resources are limited. In this sense, this paper presents a hardware-friendly algorithm for concurrently execution of anomaly detection and lossy compression for hyperspectral imaging. The proposed algorithm reuses a hardware platform to perform both tasks in parallel, offering a validated hardware architecture designed for deployment on a cost-optimized FPGA device. The experimental results show that our hardware component can process hyperspectral images with a resolution of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11554_2025_1692_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="78" /> </InlineMediaObject> <EquationSource Format="TEX">\(825\times 1024\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>825</mn> <mo>×</mo> <mn>1024</mn> </mrow> </math></EquationSource> </InlineEquation> pixels and 160 bands in 0.53&#xa0;s (486 MB/s), with a power consumption of 1.08 watts (399 MB/W). </p>

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Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月16日

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  • Julián Caba,
  • Jesús Barba,
  • María Díaz,
  • José Luis Mira,
  • Sebastián López,
  • Juan Carlos López

摘要

Hyperspectral sensors capture a wide range of spectral data, making them crucial for Earth observation applications, but this fact poses significant challenges for embedded systems with limited resources. Nevertheless, most studies only perform one application at the same time, so multi-applications in the same device are not considered since high-performance and low hardware resources are limited. In this sense, this paper presents a hardware-friendly algorithm for concurrently execution of anomaly detection and lossy compression for hyperspectral imaging. The proposed algorithm reuses a hardware platform to perform both tasks in parallel, offering a validated hardware architecture designed for deployment on a cost-optimized FPGA device. The experimental results show that our hardware component can process hyperspectral images with a resolution of \(825\times 1024\) 825 × 1024 pixels and 160 bands in 0.53 s (486 MB/s), with a power consumption of 1.08 watts (399 MB/W).