Acquisition modes

Optical imagery can be captured in a variety of acquisition modes. These modes have varying levels of complexity and can be used for different purposes.

Optical collections offer imagery that is captured in three primary acquisition modes: mono, stereo, and tri-stereo.

Mono or monoscopic acquisition mode is when an imaging system captures a single image of an area of interest (AOI). This is a relatively simple acquisition mode and can be used to obtain imagery for general visualization, mapping, and basic analysis.

Stereo or stereoscopic acquisition mode is when, during the same or subsequent pass, an imaging system captures at least two images of an AOI, with different viewing angles. The different viewpoints will still be in the same orbit. Stereo pairs provide depth information that can be used to produce digital elevation models and derive terrain elevation information.

Tri-stereo acquisition mode is a more complex type of stereo mode, where a third additional image is captured of the AOI in a near-vertical position. Tri-stereo triplets can be used for dense urban or mountainous areas, where elevation may block the line of sight of the sensor to the AOI.

Mono imagery is available on the console, but the availability of stereo and tri-stereo may differ depending on the collection. Some imagery is only available upon request.

^*Upon request.

Base/height ratio refers to how close apart stereo or tri-stereo images are taken relative to the height of the passing imaging system. It’s an important parameter for imagery that is going to be used to generate elevation models.

For automated elevation model generation, the optimum B/H ratio is 0.25. Otherwise, follow these guidelines:

• For flat regions, use a higher B/H ratio.
• For mountainous or dense urban areas, use a lower B/H ratio.

If single-pass image pairs or triplets aren’t available, mono imagery with similar inclination angles over the same AOI can be used. This will require additional co-registration steps to ensure images align correctly for any subsequent analysis. This approach is not suited for AOIs with vegetation cover, and cannot be used for subsequent vegetation management analysis.

Artificial stereo pairs can be ordered using the Python SDK. For more information, see the Searching for real and false stereo Jupyter notebook.

Stereo and tri-stereo acquisitions require imaging systems to reorient their sensors on the fly to capture images from different angles. Executing these maneuvers may not be successful on the first attempt. As a result, these types of acquisitions can take longer than a simpler mono acquisition. For more information on how order parameters affect the timeframe of your tasking order, see Tasking acquisition.

• Stereo data ordering 101
• Stereo imagery, DSM, and machine learning for vegetation management
• Utility vegetation management