IDRISI GIS and Image processing Software.

Overview

IDRISI Andes is an integrated GIS and Image Processing software solution providing over 250 modules for the analysis and display of digital spatial information. IDRISI offers the most extensive set of GIS and Image Processing tools in the industry in a single, affordable package.

Tools for land planning, decision support, and risk analysis are included side-by-side with tools for spatial statistics, surface analysis, and spatial modeling. With IDRISI, all analytical features come standard—there is no need to buy costly add-ons to extend your research capabilities.

Product Features 

Complete GIS and Image Processing in One Package: unleash the power of over 250 GIS and Image Processing modules to tackle your every environmental need for analysis.

• Land Change Modeling: quickly assess land change, predict future scenarios and assess the impacts of these predictions in this unique modeling environment
• Time Series Analysis: compare pairs of images or analyze trends and anomalies in multiple images
• Image Processing: complete restoration, transformation and supervised and unsupervised classification support for multi-spectral and hyper-spectral imaging
• Hard and Soft Classifier Support: traditional classifiers such as maximum likelihood and K-means as well as Bayesian and linear spectral unmixing soft classifier support
• Neural Network Analysis: the most extensive support for neural networks including multi-layer perceptron, SOM, and fuzzy art map classifiers
• Decision Support: the industry leader in tools for multi-criteria evaluation and multi-objective land allocation modeling
• 3-D Fly Through: simple, interactive feature that allows you to simulate movement by controlling altitude, orientation, and movement
• Surface Analysis: the most extensive tool set for raster GIS analysis including interpolation and hydrological modeling
• Spatial Statistics: multiple and logistical regression, geo-statistics, and much more
• Graphical Modeling: build and execute multi-step models graphically
• Image Calculator: interactive expression building facility for image manipulation including mathematical and logical expressions
• Extensive Conversion Tools: easily import or export with many of the most popular software and government formats
• COM Compliant: develop your own scripting from such programming environments as Visual Basic or Python to manipulate IDRISI

The Land Change Modeler for Ecological Sustainability is included with IDRISI Andes and is organized around a set of tasks for land cover change assessment, change prediction, assessment of its impacts on habitat and biodiversity, and the exploration of planning interventions. The Land Change Modeler can also be purchased separately as an ArcGIS extension.

IDRISI has incomparable classification tools. Machine learning classifiers consist of three neural network classifiers--multi-layer perceptron, self organizing map, and fuzzy art map.

 This screenshot illustrates some of the data products developed for a study on the effects of sea level rise on a coastal rice-producing area in Vietnam. Part of the study included prediction of future sea level and inundated areas. This portion of the study explicitly incorporated uncertainty in the elevation data as well as the projected sea level rise. The probability of inundation image (upper left) was then threshold based upon an acceptable level of risk. The multi-criteria analysis was a prediction model of where people might relocate after inundation, thus a predicted land cover image was produced.

IDRISI provides the most extensive set of change and time series analysis tools in the industry. This screenshot illustrates Markov Chain Analysis, a technique for predictive change modeling. Predictions of future change are based on changes that have occurred in the past.

IDRISI supports the import of HDF-EOS 4 format which is useful for importing ASTER and MODIS data.

IDRISI provides full map composition capabilities, including multiple raster and vector layers, layer blending, interactive RGB compositing, multiple legends, title, text labels, grid, north arrow, scale bar, text and image insets. This image depicts the White Mountain region in New Hampshire, USA, including data for elevation, roads, and boundaries using various layer blending options.