Built by researchers for researchers, IDRISI is designed to support the analytical requirements of the most challenging problems confronted in our stewardship of the environment as well as provide day-to-day support for the common tasks of the GIS and Image Processing community. Clark Labs has the largest proportional research and development (R&D;) budget in the industry devoted to the analytical development of geographic information technology. Significant innovations include:

Machine Learning and Neural Networks
Soft Classifiers
Multi-Criteria / Multi-Objective Decision Making
Uncertainty Management
Spatial Processes
Change and Time Series Analysis
Dynamic Modeling

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Machine Learning and Neural Networks
Clark Labs pioneered the introduction of integrated neural networks with the IDRISI Kilimanjaro Edition (Version 14). Now with the Andes Edition, Clark Labs becomes the leader in the development of the first ever machine learning procedures in a GIS and image processing system. Why are neural networks and related machine learning approaches important? Because they do not depend upon restrictive assumptions about the underlying character of class distributions and are capable of learning complex patterns with limited data. IDRISI is the premier system for integrated neural network and machine learning solutions with the introduction of:

An advanced Multi-Layer Perceptron (MLP) neural network classifier that offers a variety of innovations:
– First ever automatic mode supervised training
– First ever progressive learning rate adjustment
– First ever hidden layer mapping with linear output option

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What are the implications of these developments? The first two offer the ability to run the MLP automatically without the need for an in-depth understanding of how the MLP works. The third is but one of a range of options provided for those actively researching the potential of neural networks as analytical tools and wanting to know more about how they work.

A Self-Organizing Feature Map (SOM) neural network which uses a two-dimensional neuron topology with both supervised and unsupervised output options.
A Fuzzy ArtMap neural network with both supervised and unsupervised output options.
An integrated Decision Tree machine learning classifier based on the ID3/C4.5 algorithm.
The first implementation of the K-Means unsupervised classification procedure as a machine learning algorithm with dynamic feedback and direct training intervention.

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Soft Classifiers
IDRISI includes the most extensive set of soft classifiers in the industry. Soft classifiers express the degree of support for each of a set of potential land cover classes at each pixel location. Thus, rather than a single map of most likely class membership, a set of images (one for each class) is produced expressing the degree of support. Soft classifiers can be used for a variety of purposes including uncertainty management (i.e., Why is the classifier having difficulty classifying this pixel?) and sub-pixel classification (i.e., What are the proportions of cover types mixed into this pixel?). Specific innovations developed by Clark Labs include:

First-of-its-kind, innovative solutions to the band-limitations of linear spectral unmixing. Normally, the number of parent classes (end members) in sub-pixel classification is limited to the number of input bands. IDRISI has removed this restriction through a logical pairing of soft-classifier approaches.
The first introduction of soft classifiers based on Bayesian, Mahalanobis Typicality, Dempster-Shafer Belief and Plausibility, and Fuzzy Set membership metrics.

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Multi-Criteria / Multi-Objective Decision Making
In 1993, IDRISI introduced the first instance of Multi-Criteria and Multi-Objective decision making tools in GIS. Twelve years later, IDRISI is still the industry leader, responsible for:

The first implementation of the Ordered-Weighted Average for multi-criteria evaluation that allows one to balance the relative amount of tradeoff between criteria with decision risk in balancing discordant information.
The first implementation of the MOLA heuristic for multi-objective land allocation.
The first GIS software implementation of Saaty’s Analytical Hierarchy Process (AHP).
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Uncertainty Management
The first great horizon for GIS was conquering complexity. Computers and software have done that exceptionally well. The next great horizon is the conquest of uncertainty. Clark Labs has taken a pioneering role in this area with the following selective developments in IDRISI:

The first-ever implementation of a Dempster-Shafer evidence aggregation procedure in GIS.
The first soft reclassification procedure (PCLASS) that allows one to map the probability of a location being above or below a threshold (such as sea level rise).
A first-ever module to generate normal and rectilinear distributions for uncertainty analysis such as Monte Carlo simulation.
The only implementation of spatial prior probabilities for Maximum Likelihood classification.
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Spatial Processes
IDRISI has always been recognized as a pioneer in the analysis and modeling of spatial processes. Specific innovations include:

The only anisotropic cost distance analysis procedure in the industry. This is a procedure that recognizes that costs vary according to how you are moving through a cell. For example, moving uphill acts as a friction whereas moving downhill acts as an accelerant.
In support of anisotropic cost analysis, IDRISI introduced innovative procedures for force vector analysis including the ability to determine resultant vectors.
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Change and Time Series Analysis
IDRISI provides the most extensive set of change and time series analysis tools in the industry, including:

The pioneering and preeminent Time Series Analysis procedure based on standardized Principal Components Analysis.
The first integrated implementation of Fourier Series time series analysis.
The most comprehensive and advanced set of change analysis procedures including differencing, ratioing, regression differencing and change vector analysis techniques.
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Dynamic Modeling
IDRISI is the only system that has implemented dynamic modeling using a graphical interface. IDRISI’s Macro Modeler interface provides, without question, the premier modeling interface in the industry complete with feedback loops and dynamic layer groups for batch processing. It is so advanced that it is a primary tool in our own development of new analytical modules.