REAL-TIME QUALITY VALIDATION FOR SAR AND OPTICAL SATELLITE DATA

Onboard Data Quality Assessment

Satellite operators waste valuable downlink bandwidth on corrupted or unusable data, compromising mission efficiency and increasing operational costs. Our solution provides autonomous validation of both SAR and optical payload data in real-time before downlink, ensuring you only transmit high-quality data that meets your mission requirements.

Reduce downlinked data volume by up to 40%Eliminate transmission of unusable imageryExtend mission life through optimized resource usageIncrease effective data throughput without hardware changes

The Problem with Traditional Data Collection

1

Bandwidth wasteSatellites blindly downlink all collected data, including images compromised by cloud cover (optical), speckle noise (SAR), or other quality issues

2

Storage burdenGround systems must store and process massive amounts of data before determining what's actually usable, wasting infrastructure resources

3

Delayed insightsQuality issues aren't discovered until after downlink, causing costly delays in delivering actionable intelligence to end users

4

Missed opportunitiesLimited downlink windows are wasted on poor quality data instead of high-value imagery, reducing effective mission capacity

Data Quality Comparison

Traditional Optical Collection
40% unusable data
Cloud-covered or low quality
With Onboard Quality Assessment
~95% usable data
Quality-validated data only
Traditional SAR Collection
35% suboptimal quality
High speckle or distortion
With Onboard Quality Assessment
~90% high quality SAR
Quality-validated SAR data
EFFICIENCY GAIN
40%more usable data

How Onboard Quality Assessment Works

Autonomous quality evaluation before downlink

Step 1

Capture & Analyze

As data is collected by optical or SAR sensors, our lightweight algorithms immediately analyze key quality parameters specific to each sensor type.

Step 2

Score & Prioritize

Each data product receives a quality score based on sensor-specific metrics (cloud coverage, blur, speckle noise, etc.) and is prioritized accordingly.

Step 3

Filter & Store

Based on configurable thresholds, low-quality data is filtered out or compressed, while high-quality data is preserved at full fidelity.

Step 4

Optimize & Downlink

During downlink opportunities, the highest quality and most valuable data across all sensors is transmitted first, maximizing the value of limited bandwidth.

Technical Implementation

System Specifications
Processing Overhead
<5% of typical payload computer resources
Memory Footprint
180KB - 1.2MB depending on implementation
Supported Processors
LEON3/4, RAD750, and other radiation-hardened CPUs
Orbital Environments
Qualified for LEO, MEO, and GEO operations
Compliance
MISRA-C compliant with DO-178C considerations
Integration Options
Flight Software Integration
Integrates with major flight software platforms through standardized interfaces
Payload Processing Module
Can be deployed as a separate payload processing module with minimal modifications
API-Based Integration
Simple API for quality scoring and filtering decisions that works with existing pipelines
Custom Quality Metrics
Configurable quality metrics and thresholds based on specific mission requirements
Over-the-Air Updates
System parameters and algorithms can be updated in-orbit as mission needs evolve

Performance Benefits

Quantifiable improvements to mission operations

40%
Reduced Data Volume
Filter out unusable data before downlink to save precious bandwidth and storage
95%
Usable Data Ratio
Significantly increase the percentage of downlinked data that meets quality standards
8+ mo
Extended Mission Life
Optimize resource usage to extend effective satellite operational lifetime

Comparison with Traditional Approaches

Metric
Traditional Approach
With Quanmo
Improvement
Usable Data Ratio
45-60%
85-95%
+40% improvement
Downlink Efficiency
All data transmitted
Quality-filtered data
40% bandwidth saved
Storage Requirements
Full capacity needed
Optimized usage
38% reduction
Processing Overhead
Heavy ground processing
<5% onboard resources
Minimal impact

Success Stories

Real-world results from implementing Onboard Quality Assessment

Case 1
38% storage reduction

Multi-Sensor EO Satellite

Reduced data storage requirements by 38% while increasing usable imagery by 22% through selective filtering of cloud-covered optical data and low-quality SAR acquisitions.

Case 2
42% less unusable data

SAR Constellation

Decreased unusable data transmission by 42% through onboard speckle analysis and geometric quality assessment, resulting in more efficient use of limited downlink opportunities.

Case 3
8 months extended life

Optical Monitoring Mission

Extended operational life by 8 months through cloud-aware collection optimization and intelligent resource management without any hardware modifications.

Image Quality Improvement

A leading Earth observation company operating a multi-sensor constellation deployed Onboard Quality Assessment across their fleet. Before implementation, approximately 42% of downlinked optical imagery was significantly impacted by cloud cover, and 35% of SAR data had quality issues that made it unsuitable for customer delivery.

Immediate bandwidth optimization — Downlink volume decreased by 36% in the first month while maintaining the same amount of usable data delivered to customers

Improved SAR acquisition planning — Machine learning models trained on quality assessment data created improved acquisition plans, reducing collection in areas likely to produce low-quality data

ROI realization within 90 days — The solution paid for itself through reduced ground infrastructure costs and improved customer satisfaction within just three months of deployment

Implementation Results
Optical Quality Improvement
92%
SAR Data Usability
89%
Ground Storage Reduction
38%
Processing Cost Reduction
42%

Capabilities

How Onboard Quality Assessment transforms satellite operations

Optical Data Quality Assessment

Comprehensive quality evaluation for optical imagery

  • Cloud cover and haze detection with precise percentage measurements
  • Visual clarity and contrast evaluation across illumination conditions
  • Signal-to-noise ratio analysis across all spectral bands
  • Blur detection and sharpness scoring for precise imaging
  • Saturation and dynamic range analysis to prevent data loss
  • Multi-spectral band integrity validation for scientific applications

SAR Data Quality Assessment

Specialized analysis for radar imagery quality

  • Speckle noise measurement and mitigation assessment
  • Radiometric calibration verification for accurate measurements
  • Geometric distortion detection and correction estimation
  • Range and azimuth ambiguity identification
  • Doppler centroid estimation quality monitoring
  • Interferometric coherence prediction for InSAR applications

Adaptive Filtering System

Intelligent data filtering based on quality metrics

  • Configurable quality thresholds based on mission requirements
  • Intelligent prioritization of data packets for downlink efficiency
  • Contextual quality assessment based on collection purpose
  • Automatic anomaly detection and flagging for operator review
  • Mission-adaptive learning from ground feedback
  • Sensor-specific optimization strategies for mixed payloads

Resource Optimization

Maximize mission efficiency through intelligent resource management

  • Intelligent management of limited onboard storage capacity
  • Downlink bandwidth prioritization based on data quality and value
  • Predictive scheduling of data collection to avoid poor conditions
  • Battery and power usage optimization through selective processing
  • Processing resource allocation based on data value assessment
  • Cross-sensor trade-off management for multi-payload satellites

Integration Process

Simple deployment process with minimal impact to existing systems

Step 1: Configuration

We work with your team to define quality metrics and thresholds specific to your mission requirements and payload types.

Step 2: Integration

Our lightweight software is integrated with your flight software or payload processor with minimal modifications.

Step 3: Deployment

After thorough testing in your test environment, the solution is deployed via standard software update procedures.

Timeline

Typical integration timeline from initial engagement to operational deployment:

Week 1
Requirements
Weeks 2-3
Configuration
Weeks 4-5
Integration
Week 6
Deployment

Stop Wasting Valuable Downlink Bandwidth

Transform your satellite operations by ensuring only high-quality, usable data makes it to the ground. Maximize mission efficiency with Onboard Quality Assessment.