Matlab code for fast determination of eigenvalues of multilook polarimetric SAR data in the covariance matrix representation and for establishing the Loewner order of such matrices is given (in a zip file) with the papers
The Loewner Order and Direction of Detected Change in Sentinel-1 and Radarsat-2 Data (which describes the methods)
and
Fast matrix based computation of eigenvalues and the Loewner order in PolSAR data (which describes the fast implementations in the software).
**
If you use the code given here you must cite either of or both these papers.
**

Matlab code to perform change detection in a time series of multilook polarimetric SAR data in the covariance matrix representation is given (in a zip file) with the papers
Determining the points of change in time series of polarimetric SAR data (which describes the method)
and
Visualization of and software for omnibus test based change detected in a time series of polarimetric SAR data (which describes visualizations of change detected and software).
Such data may be obtained from spaceborne instruments such as ALOS, COSMO-SkyMed, RADARSAT-2, Sentinel-1, TerraSAR-X, or Yaogan.
**
If you use the code given here or
Dr. Morton J. Canty's
ENVI/IDL code or his
Docker/Google Earth Engine versions, you must cite either of or both these papers.
**

Matlab code to perform change detection between multilook polarimetric SAR data in the covariance matrix representation acquired at two time points, is given (in a zip file) with the paper
Change Detection in Full and Dual Polarization, Single- and Multi-Frequency SAR Data.
**
If you use this code you must cite this paper.
**

Matlab code to calculate kernel versions of
principal component analysis (PCA),
maximum autocorrelation factor (MAF)
and kernel minimum noise fraction (MNF) analysis
is given (in a zip file) with
Kernel maximum autocorrelation factor and minimum noise fraction transformations.
The code supports ENVI or ENVI-like header files.
**
If you use this software you must cite this paper.
**

Zip'ed
Matlab code to perform multivariate alteration detection
(MAD)
analysis,
maximum autocorrelation factor (MAF) analysis,
canonical correlation analysis (CCA) and
principal component analysis (PCA)
on multivariate image data
can be obtained here.
Versions supporting ENVI or ENVI-like header files
including code for the iteratively reweighted (IR-MAD) method
and associated automatic normalization,
are available also
zip'ed.
The
MAD
method was developed by
myself and
Knut Conradsen,
see also
our original MAD paper
(with James J. Simpson, University of California San Diego)
and
my IR-MAD paper
on an iterated extension to the original method.
Come back and check for new versions from time to time
(last update 20 Sep 2010;
code for non-header versions is * not* updated anymore).

Comments especially on blunders in the code are most welcome.

Dr. Morton J. Canty of FZ Jülich, Germany, has written several extensions for the ENVI remote sensing environment in IDL and Python including kernel PCA, the kernel MAF/MNF transformations, IR-MAD change detection, automatic radiometric normalization using MAD, and change detection in time series of covariance matrix multilook polSAR data. The software is freely available and is described in his textbook Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, fourth revised edition, Taylor & Francis, CRC Press, 2019.

Some of my newer code was written partly within DataBio, see also here, Data-Driven Bioeconomy (2017-2019) under the Information and Communication Technologies Call of the EU Horizon 2020 Programme. Some of both Mort's and my older code was written partly within GMOSS, Global MOnitoring for Stability and Security (2004-2008), a Network of Excellence in the Aeronautics and Space Priority of the Sixth Framework Programme of the European Union.

Here are some other programs:

I have written a few computer programs myself, and I have initiated and/or influenced work resulting in a number of programs written by colleagues, Ph.D. students and M.Sc. students. These programs center around statistics, multivariate analysis, spatial (geostatistical) data analysis, and hyper-spectral (remote sensing) tools. In several programs the data may be sampled on a regular grid as well as irregularly. The most important ones are listed alphabetically below (with co-workers/program authors mentioned).

- a2h - `rectangular' ASCII data to IMM defined irregular HIPS format
- bil - band-interleave by line to IMM defined HIPS BIL format (Rasmus Larsen)
- bioopt - bio-optical modelling by the matrix inversion method
- bip - band-interleave by pixel to IMM defined HIPS BIP format
- boxcox - calculate Box-Cox transformation
- cancorr - canonical correlations analysis (Anders Rosholm)
- cem - constrained energy minimisation (CEM), also known as matched filtering
- chi2 - read chi square test statistic and output significance level
- cloude - Cloude/Pottier decomposition of complex polarimetric radar signal
- cokrig - 2-D cokriging estimation of irregularly sampled data (Karsten Hartelius)
- cov2corr - calculate correlation matrix from variance/covariance matrix
- crosstab - contingency table (or crosstabulation) analysis
- crossv - calculate traditional 1- and 2-D cross-semivariograms, cross-covariance and cova functions of irregularly spaced data (Karsten Hartelius)
- crossv2d - calculate various types of (1- and) 2-D cross-semivariograms, cross-covariance and cova functions of irregularly spaced data
- decorr - RGB to principal components, stretch PCs, PCs to RGB
- disc - pixelwise, hierarchical and contextual classification with feature selection (Rasmus Larsen)
- distdisp - calculate distance between two variance/covariance matrices
- eqdisp - Wishart test for equal dispersion matrices
- eqmeandisp - Wishart based test for simultaneous equal means and dispersion matrices
- fuzzy - fuzzy c-means spectral and spatial cluster analysis (Klaus Baggesen Hilger and Dan Rasmussen)
- gamv2h - HIPS driver for calculation of cross-semivariograms and cross-covariance functions with GSLIB's gamv2
- histobe - histogram match to beta distribution
- hpca - Hebbian (linear) principal component analysis
- icda2.m - iterated canonical discriminant analysis for two groups
- ihs2rgb - transform a 3-frame sequence from IHS to RGB (Rasmus Larsen)
- ihsdecorr - RGB to IHS, stretch S, IHS to RGB
- imaging - statistical image analysis for Microsoft Windows (Johan Doré Hansen and Rasmus Larsen)
- imshowrgb.m - display three bands from image cube as RGB with good stretching
- kcca - kernel canonical correlation analysis (CCA)
- kcem - kernel constrained energy minimization (CEM)
- kmaf - kernel maximum autocorrelation factor (MAF) analysis
- kmnf - kernel minimum noise fraction (MNF) analysis
- kpca - kernel principal component analysis (PCA)
- ktcimf - kernel target constrained interference minimization filter (TCIMF)
- krig - 2-D (simple, ordinary or universal) kriging estimation of irregularly spaced data (Henrik Juul Hansen)
- ktb3dh - HIPS driver for 2- or 3-D (simple, ordinary or universal) kriging estimation of irregularly spaced data with GSLIB's ktb3d
- location - make location map of IMM defined irregular HIPS data
- logres - logarithmic and supremum residuals (Rasmus Larsen)
- maf - perform a wide range of multivariate orthogonal transformations such as principal component, MAF, MNF, canonical analyses, etc. (Rasmus Larsen);
- musecc - multiset canonical correlations analysis
- nndif - replace value with difference with nearest neighbour in irregularly spaced data
- osp - orthogonal subspace projection
- owaov - one-way analysis of variance
- pls - partial least squares (PLS) regression
- project - project data in hyper-dimensional feature space onto a vector
- regcovmat - regularise class dispersion matrices
- rgb2ihs - transform multiples of three frames from RGB to IHS
- rankcorr - calculate linear and rank correlations
- roprc - robust principal components analysis (Rasmus Larsen)
- saturate - saturate, standardize and stretch linearly
- simplestats - simple statistics, 1st, 2nd, 3rd and 4th order moments
- seed - growing of trainingsets for classification (Rasmus Larsen and Johan Doré Hansen)
- semivarmodel - estimate semivariogram model based on experimental semivariogram
- sep2dfilter - calculate whether a 2-D filter is separable and if so, separate
- specInfoDiv - calculate spectral information divergence (i.e., the symmetrized Kullback-Leibler divergence or relative entropy) between all spectra in two image cubes
- specInfoDivRef - calculate spectral information divergence (i.e., the symmetrized Kullback-Leibler divergence or relative entropy) between all spectra in an image cube and a reference spectrum
- sigma_n - estimate noise covariance matrix of irregularly spaced data (Karsten Hartelius)
- specInfoMeas - calculate spectral information measure (i.e., the entropy) of all spectra in an image cube
- spam - spectral angle mapper
- specang - spectral angle change detection
- standard - standardize float image to desired mean and stddev
- tcimf - target constrained interference minimization filter (TCIMF)
- unmix - full and partial spectral unmixing
- wallis - Wallis filter (Nette Schultz)
- wavetr - 1-D, 2-D or 3-D wavelet transformation
- wishart - test for equality in two comlex polarimetric radar signals (can be used to carry out change detection in polarimetric SAR data)
- wishart_det - calculate determinants of all pixels in covariance representation of polarimetric SAR image data
- wishart_change - based on the complex Wishart distribution calculate change between two polarimetric SAR images in the covariance representation
- wishart_change_dualk - based on the complex Wishart distribution calculate change between several dual pol SAR images in the covariance representation

Other programs that relate to this type of (exploratory) data analysis written by colleagues and students comprise

- epp - exploratory projection pursuit (Kristian Windfeld)
- grandtour - grand tour (Kristian Windfeld)
- acecancor - non-linear canonical correlations analysis via ACE (Kristian Windfeld)
- glcm - gray level cooccurrence matrices (J. Michael Carstensen)
- lintrans - linear/affine transformations of multivariate data (Rasmus Larsen)
- mace - non-linear multiset canonical correlations analysis via ACE (Klaus Baggesen Hilger)

(The information below is a little backdated).

With J. Michael Carstensen I maintain a collection of IMM written programs for analysis of spatial and image data. The programs come in two groups, one of which is freely distributed. All programs run under UNIX and comply with the HIPS image format (Michael Landy). HIPS comes with source code and is very open and easily extended with your own software.

List of freely distributed HIPS programs from the IMM Section for Image Analysis. (These programs are good with HIPS only and they are distributed with HIPS at the time of purchase.)

List of other HIPS programs from the IMM Section for Image Analysis.

See my homepage.