atmospheric correction – hyperspectral data

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Atmospheric Correction – Hyperspectral Data. Mirza Muhammad Waqar Contact: +92-21-34650765-79 EXT:2257. RG712. Course: Special Topics in Remote Sensing & GIS. Outlines. Hyperspectral Data Hyperspectral vs Multispectral Data Analysis - PowerPoint PPT Presentation


Thematic information extraction hyperspectral image analysis

Atmospheric Correction Hyperspectral DataCourse: Special Topics in Remote Sensing & GISMirza Muhammad EXT:2257RG712

OutlinesHyperspectral DataHyperspectral vs Multispectral Data AnalysisHyperspectral Mapping TechniquesSpectral Angle MapperMatched Matching Spectral Feature FittingBinary Encoding (BE)Complete Linear Spectral UnmixingMatch Filtering

2Format of Remote Sensing Image DataBSQ => Band SequentialImage data (pixel number and line number) of each band are separately arrangedBIL => Band Interleaved by LineLine data are arranged in the order of band number and repeated with respect to line numberBIP => Band interleaved by pixelA set of multi-band data with respect to each pixel arranged spatially by pixel number and line number

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.Format of Remote Sensing Image DataRe-calibration of Hyperspectral Data1. 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.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 HeaderNormally while conversion, wavelength information is lostBy editing header file of radiance, wavelength can be attach againRe-calibration of Hyperspectral DataFixing Bad PixelsIn 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. Fixing Bad PixelsIn level 1B1 processing => Interpolation to fill bad data valuesIn 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. Criteria for Selection of bad bandsBand 1-7, 58-76, 225-242 zeroed bandsAtmospheric Water vapour bands: 121-122, 126-127, 167-178 and 224 bad bandsThese 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.

Fix Out of Range DataDuring 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.Fixing Outlier PixelsPresence 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 outlierDetecting outliers and replacing them with neighbour medianMedian and Mean Absolute Derivation (MAD)Global MethodWeather 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 MethodAbsolute value of the difference of pixel with the median in a local neighbour is more than a given fraction of the band MAD.Local MethodAbsolute 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.Streaking

14Atmospheric Correction ModelACORN (Atmospheric Correction Now)Input radiance image (W/m2sr/m)Image data must be 16 bitOnly accept BIL or BIP formatAncillary DataOrientation ParametersSensor AltitudeDate & TimeWater Vapour AmountVisibility

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

1. Input Radiance ImageInput image must have a BIL or BIP interleave.Should contain Hyperspectral radiance data scaled into 16 bitScale 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 W/cm2/nm/sr, the gain value for each band will be 1. In our case its unit is W/cm2/m/sr10 W/m2/m/sr = 1 W/cm2/nm/sr2. Scene and Sensor InformationScene center locationSensor typeSensor altitudeGround elevationFlight dateFlight time3. Atmospheric ModelSub Arctic WinterMid Latitude WinterUS StandardSub Arctic SummerMid Latitude SummerTropical

3. Atmospheric Model

3. Atmospheric Model

4. Aerosol Model No AerosolRuralMaritimeUrban Topographic5. Aerosol RetrievalAerosol RetrievalThis option allows the retrieval of aerosols and estimation of a scene average visibility using image dark pixels.

Initial Visibility ValueAn estimate of visibility (in kilometers) at the time of acquisition must be provided. This value will be used if user will select dont retrieve aerosolSpectral PolishingSpectral 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.5. Aerosol Retrieval6. FLAASH Advanced Settings

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