DAY 1:
NTNU SmallSat: a Hyper-spectral imaging mission (Mariusz)
Goal is to do oceanography
Algae blooms may suffocate fish
And a pipeline of SmallSats
We need to know if what we do is fruitful to ocean study
Short observable time - several passes but not all are viable.
Questions from the audience
Will there be enough clear sky to get good images?
There will be holes in the clouds so you can see things through it
HSI will allow for other information that you can see with the eye
Need to emphasize that we have to perform trade studies
How do linux systems survive?
Cost of the SmallSat?
Do we specify the satellite performance criteria from the bottom or from the top?
Know the end-user requirements
Hyperspectral remote sensing (João)
Lots of questions regarding the optical characteristics - and comparison to Sentinel
Will be using JPEG 2000 and Idletechs technology compression, multivariate techniques borrowed from the field of chemometrics
The importance of storing the raw data to able to recalibrate the data is important to be able to get valid data for science.
We want to compress data to be able to downlink it
2nd order effect from Visual bands may enter into NIR bands.
There exist simplistic algorithms for agricultural or land applications (NDVI)
Don’t have HSI with ground truth validation using our camera of chlorophyll.
Need more data to build a database of endmembers (chlorophyll signatures)
Ajit Subramaniam et al.: The tale of three upwelling systems
hydrocarbon gas bubbles from boat and space
Sunglint is good for contrast when looking at sliks
Dual laser fluorometer will possibly be available for in situ observations
Gathered a lot of data from the coast of Vietnam, east-sea, falkor cruise 2016
Observing the dynamics of Oceanographic phenomena
Vertical transport hypothesis
With HSI, holy grail to better characterize phytoplanktons, community structure
Trying to develop models for upwelling from hydrocarbon gas, using acoustics, fluerometers, etc.
Milica Orlandić: onboard HSI processing
Possible to build in new state of the art HSI algorithms in the processing pipeline
Zynq board (700 series as of now)
Possible to optimize for power usage and parallelism.
PLS for further optimizing for our specific platform
FPGA for low power consumption, compact size, low weight, real-time processing.
Caveats: low abstraction, deep understanding of design, long design time
Lots of recent work using FPGAs for HSI processing
FPGAs comes at an engineering cost, but the advantages (may) outway the cost
Image data should be parallelized
Can not use regular microcontrollers due to limitations (Julian, Milicia)
Should not go for a barebones system as we need the felxibility (Joao)
Need to make the entire pipeline to be able to accurately estimate our power consumption and other crucial parameters.
Implement idle techs algorithms to be able see limitations
Need to determine algorithms to be able to adapt it to HW and integration.
There might be certain bands that are more important for certain applications and a maximum variance might not be the way to go, when looking for small changes.
The idle tech SW can be adjusted for specific applications (Joao)
What data levels can we expect?
Two pipelines, one for raw data and one for level ?2-1b? data.
Could be changed.
Need to build something in order to test it.
Julian Veisdal: HSI HW status
Off the shelf components camera with 3D printed body
USB interface is fast enough.
Interchangeable and flexible design of camera housing (3D printed)
Based on automotive / industrial parts, to be tested. Not space-tested (SpaceX)
The design is as seen in the slides, ready to be printed for testing.
Need both shock testing and thermal testing as well as good characterizing of the camera behaviour.
Desktop ready is not the same as space ready.
Emlyn Davies: Particle Measurements with help from AI of the ocean
Good presentation and presentation technique.
Measurements is done from 0.05 um to 2000 um, to get all particles
In-situ measruements, imaging micro-organism.
Using 6-layered DCC for classifying (tensorflow based).
Mapping concentration at different depths
May be able to see copepods from space, based on models in-situ measurements
The pictures shown is at 15 MB image at 70 Hz
Most of the processing and analysis is traditional image processing.
Jan Otto Reberg: Intelligent compression for SmallSat HSI mission
There will be a lot data so we should compress it in a smart way.
Idle tech has a lot of experience with compression of HSI or multivariate data
Need to make assumptions about the data
Assumed dependence between different bands i.e. some data is irrelevant
To be compressed: # of known phenomena, noise, outliers, etc.
The model for signal flow is changeable, and is based on ground and on board.
PCA caveats due to variance based approach can be circumvented by priori knowledge.
Want to retrieve information that is useful for scientific purposes, and not necessarily shannon based compression approaches wrt. Information theory.
Need to decide what kind of satellite communication protocol to use.
JPEG2000 is the preferred image compression algorithm. link
Binning will be great for HW, but might be unbeneficial for information recreation.
Use FPGA to be able to test different approaches
Concerns being raised for this method used for oceanography
Need to test it for water specific applications
Endmember and mixing of pixels may suffer under compression
Will be able to reproduce images due to using the entire spectra.
The compression does not interpret the data, it only compresses
Davide Alimonte: Collection and analysis of ocean color bio-optical data.
Good presentation about remote sensing of oceanography basics
See slides in dropbox folder
Conclusion: don’t measure when there’s higher wind speeds of 4m/s or bright sun.
Contributions to reflectance comes from all over, and not just directly below the sun.
Integration time of sensor have great effect on sensor performance
Quality assurance and validation of gathered data is paramount for oceanographers
Even large expensive satellites need to be recalibrated radiometrically
Comparison of ANN and standard Regression
30 % accuracy is great wrt. Ocean colour analysis
Atmospheric correction need to be on point, or else the data will be garbage.
The field of Remote sensing is not mature in the field of multivariate analysis (ajit)
Using HSI might be able to better model hyperspectral images.
Trygve Fossum: Adaptive data collection: What robots can do for ocean science
General about sampling the upper oceans, where to sample
Ocean models will tie together all types of sampling devices.
Combining models and sensors to generate sensing strategy to acquire desirable data
Data fusion is still under work
Remote sensing will be able to improve the data from AUVs and ASVs
The sensor equipped AUV is able to adapt its behaviour to sample the desired data
Remote sensing depth capabilites depends on what you are looking at, up to 10s meters
Should use HSI at several altitudes to confirm atmospheric correction algorthims, and other types of corrections.
Difficult to do properly, need to be done carefully
Olav Godø: Marine ecosystem function and dynamics a playground for advanced technology
Don’t think the right Powerpoint was uploaded to the dropbox folder.
A moving platform will mix the space and time dimension, which needs a model to properly separate. This is difficult if not impossible.
Need to carefully plan data sampling schemes to not regret it in hindsight
Discovers unexpected natural phenomena.
Mackerel is one of the most valuable resources in the norwegian oceans
Use LIdars for water penetrating observations
Airplanes were expensive, but lidars are now much more commercially available.
Should be used in drones, could be used i future satellites?
We are moving into a regime that is unknown with HSI, which makes it impossible to use good model to verify to begin with.
The challenges of observing the ocean is due to the high rate dynamic effects.
Acoustics are the only observations who preserve spatial and temporal considerations.
A lot of data is being used to make and validate models to observe oceanographic events.
Acoustics might be an underused type of sensor in AUVs, Gliders, buoys, etc.
Remote sensing could/should be used for verification and better model development
Difficult to merge data from different types of sensor and sensor schemes.
Validation of models is important.
Satellite observations is important.