ATMOSPHERIC CORRECTION – HYPERSPECTRAL DATA

Course: Special Topics in Remote Sensing & GIS

Mirza Muhammad WaqarContact:

mirza.waqar@ist.edu.pk+92-21-34650765-79 EXT:2257

RG712

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

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

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 Data

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.

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

Re-calibration of Hyperspectral Data

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.

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.

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.

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.

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

Median and Mean Absolute Derivation (MAD)

1. 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.

2. 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.

3. 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.

Streaking

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

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

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

2. Scene and Sensor Information

1. Scene center location2. Sensor type3. Sensor altitude4. Ground elevation5. Flight date6. Flight time

3. Atmospheric Model

1. Sub Arctic Winter2. Mid Latitude Winter3. US Standard4. Sub Arctic Summer5. Mid Latitude Summer6. Tropical

3. Atmospheric Model

3. Atmospheric Model

4. Aerosol Model

1. No Aerosol2. Rural3. Maritime4. Urban 5. Topographic

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

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.

5. Aerosol Retrieval

6. FLAASH Advanced Settings

Questions & Discussion