Combining Resolution and Number of Looks

What is Resolution?

In Canopy, we define resolution as the ground plane resolution of your final collected image. A higher resolution means the system can resolve finer details more clearly, enabling it to distinguish closely spaced features or data points within the same collection. This capability is crucial for identifying and analyzing specific geographical or structural characteristics accurately.

What is Number of Looks and Multi-looking?

In Canopy, the number of looks describes the number of sub-apertures that a collect can be decomposed into such that each sub-aperture has the tasked azimuth resolution. In simpler terms, this means dividing the radar data into multiple segments and combining them to enhance the image quality by increasing clarity and reducing noise. A collection may be single-looked or multi-looked.

Multi-looking can significantly improve data quality through two main effects:

Reducing signal and noise variance
Observing additional scattering center from objects in the scene

This means that employing more multi-looking can lead to images where the texture of clutter is clearer, shadows are more distinguishable, and more components of an object are observed.

The process of multi-looking not only helps in reducing signal variance (also known as speckle), making images easier to interpret, but also increases the potential for detecting man-made features or objects like vehicles, thanks to the additional glints captured. This can help with identifying objects that do not naturally reflect much radar signal back to the satellite, such as certain types of vehicles like aircraft, or natural terrain features. This enhancement is especially useful if you require high-precision data to make informed decisions, whether for environmental monitoring, urban planning, or security purposes.

How to Choose the Right Resolution and Multi-looking Combinations

Combining resolution and multi-looking allows for strategic choices tailored to your specific needs for image clarity, detail, and application requirements. The decision between options, such as a 1 m resolution with 4 looks or a 0.25 m resolution with a single look, should be guided by your project's specific requirements for detail, noise tolerance, and application goals.

0.25 m - 0.35 m with Minimal Multi-looking: For applications where capturing the finest details with the highest clarity is paramount, even if some level of speckle is present in the images.

Benefits:
- Maximizes detail capture: by focusing on the highest resolution without averaging out the details through multiple looks.
Trade-Offs:
- Increased Speckle: Utilizing minimal multi-looking does not sufficiently mitigate the speckle inherent in SAR images. This results in less clarity in clutter and shadows.
- Low terrain-to-noise (TNR) ratio: When finer resolution collects are single-looked they have a lower TNR score therefore a greater impact from noise.

0.25 m - 0.35 m with High Multi-looking: Ideal for scenarios requiring both high detail and superior image quality. This combination balances fine detail with image smoothness.

Benefits:
- Significant Speckle Reduction: Achieves a high level of detail while substantially reducing speckle, facilitating complex analyses with cleaner, clearer images.
- Enhanced Object Profiling: Multi-looking from various angles can improve the three-dimensional profiling of objects, providing a more nuanced view of their structure and orientation.
Trade-offs:
- Potential for Subtle Detail Loss: The averaging of multiple images can lead to slight blurring, potentially diminishing the visibility of ultra-fine details.
- Blurring of Moving Objects: The process can cause blurring when tracking moving objects, as different captured looks may record the object in slightly shifted positions. This can impact the precision of object identification and tracking.

0.5 m - 1 m Resolution with Minimal Multi-looking: Ideal for quick surveys and reconnaissance over extensive areas where the priority is broad coverage and rapid tasking rather than detailed scrutiny.

Benefits:
- Pricing: These resolutions offer the most cost-effective options available. See our pricing sheet.
- Higher Area Coverage Opportunity: Enables more tasking opportunity within a single pass. This efficiency is particularly valuable for initial surveys, allowing for broad assessments over vast regions quickly.
Trade-offs:
- Higher Noise Levels: The minimal use of multi-looking does not sufficiently reduce the speckle noise, resulting in grainier images.
- Reduced Clarity: This setup is less effective at distinguishing subtle features or variations within the scene, which might complicate the identification of finer details.

0.5 m - 1 m Resolution with High Multi-looking: ideally suited for broad observational tasks. It's also effective for classifying larger objects, such as airplanes, where enhanced image smoothness helps distinguish these objects more clearly. High multi-looking substantially reduces noise, thus improving clarity and simplifying the interpretation of large-scale phenomena.

Benefits:
- Enhanced Image Quality: Multiple looks significantly reduce noise, which sharpens the overall image quality, facilitating easier detection of broad patterns and trends across extensive areas.
- Improved Object Classification: The noise reduction helps in better distinguishing larger, static objects within the scene, making this approach suitable for applications like aircraft classification in wide area surveillance.
Trade-offs:
- Potential Blurring of Dynamic Features: Fast-changing phenomena within the scene might appear blurred. This occurs because the multiple captures required for high multi-looking are taken over an extended period, during which significant changes can occur.

Recommended IPR and Look combinations per Use Case

The table below provides recommendations for resolution and number of looks tailored to a variety of use cases. These settings are designed to ensure you receive the most useful and detailed data for your specific project requirements.

Keep in mind that these are broad-strokes suggestions and might not be universally applicable to the nuances of your mission. We've geared our recommendations towards providing high fidelity data, but there may be instances where you can opt for less resolution in favor of higher volume of coverage.

Use Case	Res/Looks	Notes
Broad Observation Examples		Ideal for large area monitoring and when detailed object recognition is not critical.
Vessel Detection	1 m @ 1 Look	Optimal for tracking and identifying if vessels are present over wide maritime expanses. See our detailed observations examples for classifying vessels.
Broad Border Monitoring	0.5 m @ 1 Look	Suitable for maintaining a general watch over extensive land borders. 1 m may also yield good results. If looking at ground disturbances then multi-looking may be required at lower graze angles if the goal is to see disturbances in clutter. For more granular analysis see Detailed Border Monitoring below.
Forest Monitoring	0.5 m @ 1 Look	Useful for observing large tracts of forested land for changes in coverage or health. 1 m may also yield good results.
Disaster Response	0.5 m @ 1 Look	For assessing the scope of damage across affected areas such as flooding in an urban area. 1 m may also yield good results.
Detailed Observation Examples		Best for scenarios where high-detail object recognition and analysis are necessary.
Aircraft Detection	0.5 m @ 5 Looks	Multi-looking enhances the clarity for detailed profiling of planes in airfields. Consider a higher resolution for smaller aircrafts.
Ship Classification	0.35 m @ 1 Look	High resolution needed for classifying ships based on their structural characteristics. A higher graze can further help with classification. For larger ships, 0.5 m or 1 m may suffice. Reducing multi-looking is advisable for moving targets to minimize blurring. Docked, stationary ships may benefit from some multi-looking to increase profiling and clarity.
Detailed Border Monitoring	0.35 m @ 4 Looks	High resolution, combined with multi-looking and/or a high grazing angle allows for close monitoring and classification of activities. Consider reducing multi-looking for moving targets.
Counting & classifying vehicles	0.25 m @ 3 Looks	Multi-looking helps define and distinguish assets; for instance, closely parked cars. 0.35 m that has been multi-looked will also suffice with distinguishing two signatures, but provides less granular detail—for instance, which way a car is parked.

ℹ️
If you're unsure about the best parameters for your use case or need further assistance, don't hesitate to reach out to our support team. Our experts are on hand to provide guidance and answer any questions you may have, ensuring you make the most informed decision for your project.

Multi-looking Examples

Ferrari World

1-look

2-look

4-look

8-look

1-look

2-look

4-look

8-look

Elmendorf Air Base

1-look

2-look

4-look

8-look

Airbase 101, Niger

1-look

2-look

4-look

8-look

Kawar Naval Base

1-look

2-look

4-look

8-look