IDL Extensions for ENVI

Mort Canty

This page lists the most recent versions of my IDL programs for the ENVI environment discussed in my textbook
Image Analysis, Classification and Change Detection in Remote Sensing, with Algorithms for ENVI/IDL, Second Revised Edition
Taylor & Francis, CRC Press 2010.
See also Allan Nielsen's software page for Matlab versions of the change detection algorithms.

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Prerequisite Libraries

The following libraries must be present in the IDL path before attempting to run any of the extensions:

David Fanning's Coyote Library

My auxilliary routines. (Documentation) (updated October 25, 2009) On windows systems place PROVMEANS.DLL and PROVMEANS.DLM from this library in your DLM path. If you're not running on Windows, see the textbook for instructions. Please note: The old PROV_MEANS.DLL is no longer used by the present IR-MAD routine.

All extensions also assume that ENVI is up and running. Most of them can be integrated directly into the ENVI main menu by copying the programs with filenames of the form program_RUN.PRO to ENVI's SAVE_ADD directory.

In addition some of the extensions can take advantage of the Tech-X Corp. GPULib interface to nVidia's CUDA. (These extensions will now also run without GPULib/CUDA.)

Extensions (Documentation)

ENVI/IDL Extensions
Preprocessing	DWT fusion	sharpen multispectral images with discrete wavelet transform
	A trous fusion	ditto with a trous wavelet transform
	Wang-Bovik quality index	evaluate radiometric fidelity of pansharpened images
	C-correction	correct for solar illumination in rough terrain
	Kernel PCA	perform nonlinear principal components analysis (can take advantage of GPULib)
	Contour-match	get tie-points for image-image registration from invariant features
Supervised classification	Bayes maximum likelihood	wrapper for the ENVI ML classifier
	Support vector machine:	wrapper for the ENVI SVM classifier
	Hybrid two-layer neural network	trained with kalman filter and scaled conjugate gradient algorithms
	Two-layer neural network	trained with scaled conjugate gradient algorithm (can take advantage of GPULib)
	Boosted three-layer neural network	apply adaptive boosting (AdaBoost) to a sequence of neural networks
	Gaussian kernel classification	non-parametric Parzen-window classification (can take advantage of GPULib)
	Probabilistic label relaxation	perform postclassification filtering
	Contingency table	calculate confusion matrices and kappa values
	McNemar test	compare classifiers with the McNemar statistic
Unsupervised classification	Expectation maximization	cluster image data with a mixture of multivariate Gaussians (can take advantage of GPULib)
	FKM clustering	cluster image data with a fuzzy K-means algorithm
	HCL clustering	cluster image data with a heirarchic agglomerative algorithm
	Kernel K-means	cluster image data with a kernel version of K-means (can take advantage of GPULib)
	Kohonen SOM	visualize image data with the Kohonen self-organizing map
	Mean shift	segment images with mean-shift algorithm
Change detection	IR-MAD	apply iteratively re-weighted multivariate alteration detection (updated 10/25/09)
	Radcal	perform automatic relative radiometric normalization of images
	MadView	set thresholds on MAD images
Miscellaneous	Structure height	use RFMs to determine height of vertical structures
	Examples	example IDL programs from the 2nd edition
	Solutions	some solutions to the progamming exercises in the 2nd edition