Atmospheric Correction – Hyperspectral Data

1. Atmospheric Correction – Hyperspectral Data Mirza Muhammad Waqar Contact: mirza.waqar@ist.edu.pk +92-21-34650765-79 EXT:2257 RG712 Course: Special Topics in Remote Sensing & GIS

2. Outlines • Hyperspectral Data • Hyperspectral vs Multispectral Data Analysis • Hyperspectral Mapping Techniques • Spectral Angle Mapper • Matched Matching • Spectral Feature Fitting • Binary Encoding (BE) • Complete Linear Spectral Unmixing • Match Filtering

3. Format of Remote Sensing Image Data • BSQ=> Band Sequential • Image data (pixel number and line number) of each band are separately arranged • BIL => Band Interleaved by Line • Line data are arranged in the order of band number and repeated with respect to line number • BIP => Band interleaved by pixel • A set of multi-band data with respect to each pixel arranged spatially by pixel number and line number

5. Format of Remote Sensing Image Data • For colour image output, BSQ format would be convenient because three bands will be assigned to R(red), G(green) and B(blue). • However BIP format would be better for classification by a maximum likelihood classifier because multi-band data are required pixel by pixel for the multi-variable processing. • BIL would be a compromise between BSQ and BIP.

6. Re-calibration of Hyperspectral Data • 1. VNIR Bands (1-70) • FIX(100.0*(float(b1)/40.0)+0.5) • This will be the VNIR bands converted to absolute radiance and scaled by 100. The output file type is BSQ. • 2. SWIR Bands (71-242) • FIX(100.0*(float(b2)/80.0)+0.5) • This will be the SWIR bands converted to absolute radiance and scaled by 100. Again the output type is BSQ.

7. Re-calibration of Hyperspectral Data • 3. Combine VNIR and SWIR Radiance • After successful conversion, VNIR and SWIR Radiance files will be combine together for further processing • 4. Attach the Wavelength Header • Normally while conversion, wavelength information is lost • By editing header file of radiance, wavelength can be attach again

8. Fixing Bad Pixels • In pushbroom sensors such as Hyperion, poorly calibrated detectors in the VNIR or SWIR arrays will leave vertical stripes or ‘streaks’ in certain pixels of an image bands. • The most extreme cases of these pixels contain little or no valid data and these are identified as bad bands.

9. Fixing Bad Pixels • In level 1B1 processing => Interpolation to fill bad data values • In level 1R processing => List of bad data values is provided with the data • Note: • Due to the geometry fix between the VNIR and SWIR bands the last pixel is left blank for all SWIR bands.

10. Criteria for Selection of bad bands • Band 1-7, 58-76, 225-242 zeroed bands • Atmospheric Water vapour bands: 121-122, 126-127, 167-178 and 224 bad bands • These bands contain little or no information about the surface. • Zeroed and bad bands will be ignored for further processing. • It is recommended to make a separated image of good bands, it will reduce data volume and speed up the processing.

11. Fix Out of Range Data • During re-scaling of the signed integer data to radiance times 100, some bright features like cloud tops cross the max data value limit. • In order to fix this issue, an offset of -1000 can be applied.

12. Fixing Outlier Pixels • Presence of outliers in the data will affect the column statistics for data bands and hence influence the destreaking based on statistical balancing. • Median and Mean Absolute Derivation (MAD) • Use to fix outlier • Detecting outliers and replacing them with neighbour median

13. Median and Mean Absolute Derivation (MAD) • Global Method • Weather the absolute value of difference of pixel with the median of the band is more that a given fraction of the band MAD. • Local/Global Method • Absolute value of the difference of pixel with the median in a local neighbour is more than a given fraction of the band MAD. • Local Method • Absolute value of the difference of the pixel with the median in a local neighbour hood is more than a given fraction of MAD in the same LOCAL neighbourhood.

14. Streaking

15. Atmospheric Correction Model • ACORN (Atmospheric Correction Now) • Input radiance image (W/m2sr/µm) • Image data must be 16 bit • Only accept BIL or BIP format • Ancillary Data • Orientation Parameters • Sensor Altitude • Date & Time • Water Vapour Amount • Visibility

16. FLAASH (Fas Line-of-Sight Atmospheric Analysis of Spectral Hupercube)

17. 1. Input Radiance Image • Input image must have a BIL or BIP interleave. • Should contain Hyperspectral radiance data scaled into 16 bit • Scale factor should be chosen such that the input image divided by the scale factor covert 16 bit data to floating point radiance. • If the image is already in µW/cm2/nm/sr, the gain value for each band will be 1. • In our case its unit is W/cm2/µm/sr • 10 W/m2/µm/sr = 1 µW/cm2/nm/sr

18. 2. Scene and Sensor Information • Scene center location • Sensor type • Sensor altitude • Ground elevation • Flight date • Flight time

19. 3. Atmospheric Model • Sub Arctic Winter • Mid Latitude Winter • US Standard • Sub Arctic Summer • Mid Latitude Summer • Tropical

20. 3. Atmospheric Model

21. 3. Atmospheric Model

22. 4. Aerosol Model • No Aerosol • Rural • Maritime • Urban • Topographic

23. 5. Aerosol Retrieval • Aerosol Retrieval • This option allows the retrieval of aerosols and estimation of a scene average visibility using image dark pixels. • Initial Visibility Value • An estimate of visibility (in kilometers) at the time of acquisition must be provided. • This value will be used if user will select don’t retrieve aerosol

24. 5. Aerosol Retrieval • Spectral Polishing • Spectral polishing is a post processing option for smoothing the output reflectance image. • Residual noise and artifacts are reduced to make the spectra appear more like the true spectra of the surface. • A value of 9 for width is recommended for typical 10 nm-resolution hyperspectral sensor.

25. 6. FLAASH Advanced Settings

26. Questions & Discussion