Skip to content

Line-by-line denoising of hyperspectral images using a minimum noise fraction-based approach.

License

Notifications You must be signed in to change notification settings

ntnu-bioopt/mnf

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

32 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Line-by-line noise removal of hyperspectral images using an algorithm based on the MNF transform.

If you use this software, we would be grateful if you could cite the following paper:

A. Bjorgan, L. L. Randeberg, "Real-time noise removal for line-scanning hyperspectral devices using a Minimum Noise Fraction-based approach", Sensors 15(2), pp. 3362-3378 (2015). doi:10.3390/s150203362.

Underlying theory can be found in the same reference.

Running

mnf is run from the command line. ./mnf filename --option1 --option2..., or mnf.exe filename --option1 --option2 ... . See ./mnf --help for options. BIL-interleaved ENVI images are assumed.

The line-by-line algorithm is run when the --line-by-line option is set. When it is not set, the ordinary MNF transform is run.

The conventional MNF transform is based on Green, A. A., Berman, M., Switzer, P., and Craig, M. D., "A transformation for ordering multispectral data in terms of image quality with implications for noise removal", IEEE Transactions on Geoscience and Remote Sensing 26(1), pp. 65-74 (1988).

Building

This application is built using CMake, producing mnf, the main application executable, and libmnf, for using the algorithms in other applications.

  1. mkdir build
  2. cd build
  3. cmake ..
  4. make
  5. make install

The output of cmake .. should give indication of missing libraries or the need to add libraries to the CMakeLists.txt file.

Requirements

This program requires a LAPACKE and BLAS implementation.

MKL can be enabled over the standard BLAS implementations available on the system by running cmake -DUSE_MKL_LIBRARIES=True .. and rebuilding.

GNU Regex is required for src/readimage.cpp.

About

Line-by-line denoising of hyperspectral images using a minimum noise fraction-based approach.

Resources

License

Stars

Watchers

Forks

Packages

No packages published