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 my 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). If you use this software do not forget to acknowledge the source. If you use the function(s) for IR-MAD (also called iMAD) analysis you must cite my IR-MAD paper, if you use the function(s) for IR-MAD/iMAD normalization you must cite the IR-MAD normalization paper by Morton J. Canty and myself.

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 and automatic radiometric normalization using MAD. The software is freely available on his home page and is described in his textbook Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, third revised edition, Taylor & Francis, CRC Press, 2014.

Some of both Mort's and my code was written partly within GMOSS, Global MOnitoring for Stability and Security, 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).

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

plus several others (which may be added later).
(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.