Convert digital number to radiance
The function divides the input image into distinct blocks,
processes each block, and then concatenates the processed output of each block to form the
output matrix. Hyperspectral images are multi-dimensional data sets that can be too large to fit
in system memory in their entirety. This can cause the system to run out of memory while running
dn2radiance function. If you encounter such an issue, perform block
processing by using this syntax.
dn2radiance(hcube,'BlockSize',[50 50]) divides the input
image into non-overlapping blocks of size 50-by-50 and then computes the radiance values for
pixels in each block.
To perform block processing by specifying the
pair argument, you must have MATLAB R2021a or a later release.
This function requires the Image Processing Toolbox™ Hyperspectral Imaging Library. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.
Read hyperspectral data into the workspace.
hcube = hypercube('EO1H0440342002212110PY_cropped.hdr');
Determine the bad spectral band numbers using the
BadBands parameter in the metadata.
bandNumber = find(~hcube.Metadata.BadBands);
Remove the bad spectral bands from the data cube.
hcube = removeBands(hcube,'BandNumber',bandNumber);
Compute the radiance values using the
newhcube = dn2radiance(hcube);
Read and display a spectral band image in the input and the output radiance data.
inputBand = hcube.DataCube; radianceBand = newhcube.DataCube; band = 80; figure subplot(1,2,1) imagesc(inputBand(:,:,band)) title('Input Band') axis off subplot(1,2,2) imagesc(radianceBand(:,:,band)) title('Radiance Band') axis off colormap gray
hcube— Input hyperspectral data
Input hyperspectral data, specified as a
DataCube property of the
object stores the hyperspectral data cube. To convert the pixel values in digital
numbers to radiance values, the
Metadata property of the
hypercube object must contain the
blocksize— Size of data blocks
Size of the data blocks, specified as a 2-element vector of positive integers. The elements of the vector correspond to the number of rows and columns in each block, respectively. The size of the data blocks must be less than the size of the input image. Dividing the hyperspectral images into smaller blocks enables you process large data sets without running out of memory.
blocksize value is too small, the memory usage
of the function reduces at the cost of increased execution time.
blocksize value is large or equal to the input
image size, the execution time reduces at the cost of increased memory
'BlockSize',[20 20] specifies the size of each data block
newhcube— Output hyperspectral data
Output hyperspectral data, returned as a
hypercube object. The
pixels values of the output data cube are radiances specifying the amount of radiation
from the surface being imaged. The radiance values are computed from digital numbers by
using the equation:
Gain and Bias are the gain and offset values
for each spectral bands respectively. The
Metadata property of
hypercube object contains the gain and the offset values.