Knoten

A library to leverage python wrapped Community Sensor Models (CSMs) for common spatial/sensor operations and testing.

References:

• CSM (usgscsm): https://github.com/USGS-Astrogeology/usgscsm
• Abstraction Layer for Ephemerides (ALE): https://github.com/USGS-Astrogeology/ale

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Overview

We currently use Knoten to help test our supported CSM implementations against well established ISIS3 camera models. In short, The CSM standard, now at version 3.0.3, is a framework that provides a well-defined application program interface (API) for multiple types of sensors and has been widely adopted by remote sensing software systems (e.g. BAE's Socet GXP, Harris Corp.'s ENVI, Hexagon's ERDAS Imagine, and recently added to the NASA AMES Stereo Pipeline [ASP]). Our support for CSM is explained in this abstract and a recently submitted paper (not yet available). Currently, we support Framing and Pushbroom (line scanner) types of sensor models in the usgscsm library.

A secondary requirement for our CSM implementation requires an ALE-generated Image Support Data (ISD). ISDs contain the SPICE-derived positional (and when needed velocity) description for each image. You can find several generated JSON-formatted examples here

Please see the status report below for the current instruments we have implemented and how well they match our ISIS3 camera models. In the near future, we will continue to address the pixel offsets we currently see. Both the CSM implementations (usgscsm) and ALE are currently in active development and both will be updated as needed to decrease these errors. Thus, none of the instruments have been tested enough for full production use.

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Installing

You can install the latest build via conda.

conda install -c usgs-astrogeology -c conda-forge knoten

You can also do a local install using the following steps within a clone of the repository.

1. Install the dependencies.
   Note: creating the environment may take around and hour. If conda is too slow, try using mamba instead.

   conda env create -f environment.yml
   

2. Install the package

   python setup.py install
   

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Building Docs

To build the docs:

1. Open the docs directory: cd docs

2. Create (if not existing) and activate the environment (or use the main knoten environment if it is already created).

   conda env create -f environment.yml #If it does not exist already
   conda activate knoten-docs

3. Run sphinx-build -b html . public to build the docs in the docs/public directory (or change "public" to a subdirectory name of your choosing).

4. Browse to the index.html file in docs/public to open the docs. If needed, run a utility like http-server in the docs/public directory to serve the docs on localhost.

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Status Report - November 2019

For full testing reports and example usage, please see the linked example Jupyter notebooks in the table below.

Instrument	Jupyter Notebooks	Production Ready	Difference CSM -> ISIS (in pixels)	Difference ISIS -> CSM (in pixels)
MRO HiRISE	link	sub-pixel; in testing for production	sample mean=-2.0e-05; line mean=2.5e-08	sample mean=-3.0e-08; line mean=1.2e-04
MRO CTX	link	nearly sub-pixel; still in research	gross error in line	sample mean=0.0002; line mean=-0.07
MEX HRSC	link	sub-pixel; in testing for production	sample mean=0.000038; line mean=-0.000072	sample mean=-0.000038 ; line mean=-7.512e-05
LROC NAC	link	sub-pixel; in testing for production	sample mean=-0.003; line mean=-0.0006	sample mean=0.0005 line mean=0.003
Kaguya Terrain Camera	link	barely sub-pixel; in testing	sample mean=0.0001; line mean=0.00003	sample mean=0.009; line mean=-1.242
Messenger MDIS NAC	link	sub-pixel; in testing for production	sample mean=-0.01; line mean=-0.003	sample mean=0.01; line mean=0.003
Cassini ISS NAC	link	sub-pixel; in testing for production	sample mean=-0.001; line mean=0.01	sample mean=0.001; line mean=-0.01
Cassini ISS WAC	link	sub-pixel; in testing for production	sample mean=0.001; line mean=0.004	sample mean=-0.001; line mean=-0.004
Dawn Framing Camera	link	sub-pixel; in testing for production	sample mean=-0.02; line mean=0.003	sample mean=0.02; line mean=-0.003

The Difference column (CSM -> ISIS) represents the mean difference in pixels from running usgscsm's image2ground and then back to the camera using ISIS3's campt (ground2image). The Difference column (ISIS -> CSM) is simply the reverse starting with ISIS3 first with campt (image2ground) and then usgscsm's ground2image.