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  1. NTNU SmallSat Lab For Students Past Projects
  2. Project and master assignments 2022
  3. Topics on Hyperspectral Image Encoding

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Topics on Hyperspectral Image Encoding

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  • Project and Master Subjects 2025-2026
  • Past Projects
    • Project and Master Subjects 2024-2025
    • Project and Master Subjects 2023-2024
    • Project and master assignments 2022
      • Ocean Color Data Analysis
      • Software Development for Optical CubeSat Payload
      • Ensemble Biomass Estimation
      • Topics on Hyperspectral Image Encoding
      • Atmospheric Correction of HYPSO-1
      • Remote sensing data fusion for algae detection
      • HYPSO hyperspectral satellite data fusion with in-situ sensors
      • HYPSO-1 data georectification using direct and indirect methods
      • Generation and calibration of HYPSO-1 data products
      • Sentinel satellite multispectral data to aid HYPSO-1 imaging
      • Verification and validation of HYPSO-2 optical payload
      • HYPSO-2: Designing a Software-defined-radio (SDR) application experiment for communication between on-ground sensor systems
      • HYPSO-2: Designing experiment for channel characterization using the Software-defined-radio (SDR) payload in HYPSO-2
      • HYPSO - Space environment effects on hyperspectral imager: performing thermal experiments and modelling
      • Software Development for Optical CubeSat Payload
      • Re-design and re-configuration for hardware-software test-bench for HYPSO-1 and HYPSO-2 (FlatSat)
      • Automation of operations for the HYPSO-1 satellite
      • HYPSO - Georeferencing, operations - Incorporating ADCS telemetry into the OPU
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Topics On Hyperspectral Image Encoding

CubeSat systems are often characterized by low data rates, while hyperspectral images are characterized by high data volume. Thus, we want to investigate various techniques to encode and compress the acquired data. In [1,2,3] a good overview of the topic is provided, but the project would be tailored to the students own interest. One desriable venue is to investigate the theqniques desciribed in [3] with more recent approaches for dimensionality reduction.

For more information or a short discussion, contact sivert.bakken@ntnu.no

  1. https://doi.org/10.1109/MGRS.2014.2352465
  2. https://doi.org/10.1109/MGRS.2020.3048443
  3. https://doi.org/10.1109/ICIP.2003.1247041

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