Electro-Optical Imagery on AWS - Electro-Optical Imagery Reference Architecture

Electro-Optical Imagery on AWS

Publication date: May 13, 2021 (Diagram history)

This diagram demonstrates how to extract, process, and store electro-optical satellite imagery by using AWS.

Electro-Optical Imagery on AWS Diagram 1


        Reference architecture diagram showing how to process electro-optical imagery on
          AWS.
  1. Demodulate and decode: Extract baseband waveform from modulated carrier; remove forward error correction.

  2. Convert into raw sensor data: Decommutate signal frames; decrypt data.

  3. Process raw images and perform QA review.

    • QA review: Confirm Images are sufficient for processing.

    • AWS Batch: Run multiple jobs in parallel.

      • AWS Fargate and AWS Lambda:

      • Sensor correction: Apply corrections for optical distortions.

      • Orthorectify: Sensor perspective.

      • Georeference: Apply image to spatial grid and assign known coordinate system.

      • Generate thumbnails: Create post-processed thumbnails for customer purchase.

  4. Store metadata: Store information on latitude/longitude collection, Region collection, time and date of retrieval.

  5. Storage: Store preprocessed images in a variety of Amazon Simple Storage Service (Amazon S3) services by balancing cost savings and time of retrieval

  6. Post processing and analysis: Complete imagery processing.

    • Feature extraction: Identify features in images (such as ships).

    • Naming/tagging of features: Tag features by name or identification system.

    • Time series creation: Tag images to sort by time.

  7. Storage and dissemination: Final storage of images and analytics for end customer.

  8. Customer delivery: Deliver final images to end customers.

Electro-Optical Imagery on AWS Diagram 2: Classified Processing


        Reference architecture diagram showing how to process electro-optical imagery on
          AWS (classified processing).
  1. Demodulate and decode: Extract baseband waveform from modulated carrier; remove forward error correction.

  2. Convert into raw sensor data: Decommutate signal frames; decrypt data.

  3. Immutable transaction log: Cryptographically establish provenance and fidelity.

  4. Optional classified processing: Throughout the image processing, move data to the appropriate regions for classified processing.

  5. Process raw images: Process raw images and perform QA review:

    • QA Review: Confirm Images are sufficient for processing

    • AWS Batch: Run multiple jobs in parallel.

    • AWS Fargate and AWS Lambda:

      • Sensor correction: Apply corrections for optical distortions.

      • Orthorectify: Sensor perspective.

      • Georeference: Apply image to spatial grid and assign known coordinate system.

      • Generate thumbnails: Create post-processed thumbnails for customer purchase.

  6. Store metadata: Store information on latitude/longitude collection, Region collection, time, and date of retrieval.

  7. Storage: Store preprocessed images in a variety of Amazon S3 services by balancing cost savings and time of retrieval.

  8. Post processing and analysis: Complete imagery processing.

    • Feature extraction: Identify features in images (such as ships).

    • Naming/tagging of features: Tag features by name/identification system.

    • Time seriesc reation: Tag images to sort by time.

  9. Storage and dissemination: Final storage of images and analytics for end customer.

  10. Customer delivery: Deliver final images to end customers.

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Initial publication

Reference architecture diagram first published.

May 13, 2021

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