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Colour Correction using Histogram Stretching
- 1. Colour Correction using Histogram Stretching Part of series on Integrating space assets in airport management and operation GLOBAL AVIATION INNOVATION INCREASING FLIGHT SAFETY USER FRIENDLY SOFTWARE SOLUTIONS COMPLIANCE ASCEND XYZ
- 3. Scope The challenge The input data is not distributed in the colour spectrum. The solution Ascend uses statistics to stretch input data to the full colour spectrum and tests hypothesis on disregarding cloud information to improve visual appeal. Original Image clipped by region and clouds shown.
- 4. Naive histogram stretching • Data is stretched using mean ± 2 standard deviations • Performed band-wise • Statistics are calculated from all data
- 5. Original RGB Band (16 bit) Results : Huge Improvements Naive Histogram Stretching
- 6. Vector data Using Clouds to improve algorithm Area of interest given by GeoJSON file and clouds given by GML. These are then put on top of the original RGB image, to show clouds on map. Roskilde Airport is in the middle of the map
- 7. Histogram stretching of cloud data • Data is stretched using mean ± 2 standard deviations • It is performed band-wise • Statistics are calculated only from areas not covered by clouds Take it to the next level?
- 8. Improved Histogram Stretching omitting clouds from statistics Naïve Histogram Stretching Improvements are seen
- 9. Original Sentinel RGB Bands Naive Histogram Stretching Another Example
- 10. Another Example Naive Histogram Stretching Histogram Stretching discarding cloud data for statistics
- 11. Experiment: Small non cloud area used Naive Histogram Stretched dataCloud Mask: Green is clouds, grey is no clouds.
- 12. Example: When the visibility is better some places, and worse in other places. Naive histogram Stretching Histogram without cloud data
- 13. Discussions The naive histogram stretching clearly improve visual appeal for end users and has been implemented for first release. Does the improved histogram stretching, using disregard of clouds in data statistics for the histogram stretching, improve the visual experience for the end user? Is it worth the resources to move the improved algorithm into production? How do we reduce the time from moving research experiments into production? What could make the visibility even better?
Editor's Notes
- Presentation on Colour Correcting / enhancement on ESA Sentinel 2 Satellites. We made a naïve and quick implementation using statistics for the full image and have been testing if improvements can be made.
- Histogram Stretching is when mapping data from one range to another, and in this case we calculate the band statistics for mean and standard deviations, and then stretching the mean +- 2 std data into the 0-255 range. This ensures that recorded noise and outliers are removed and the significant data will be distributed across the full data range.
- We did this to ensure our end users could have some visual appealing images to look at instead of the raw data which was interpreted rater dark.
- Introduction to statistics and histogram stretching given. 95% of the data is captured at 2 standard deviations.
- The quick and naïve histogram stretching is improving visual appeal a lot. When we talk about the naiv method as quick, this is the reflection on time/effort to bring it into production and make it available to end users. Not the run time of the algorithm.
- Introduction to sentinel data and clouds.
- We will see even better contrasts now that all the statistics from data covered by clouds are discarded. The hypothesis that this data do not contribute in any meaningful way due to the data being exposed to light seem to be correct.
- Its hard to make tests that tell if one is better than the other, due to it all comes down to the opinion of the user. What do you think?