Image Processing in IDRISI Selva
IDRISI includes the largest suite of tools in the industry for the processing of remotely sensed imagery. These tools are integrated within IDRISI and accompany IDRISI’s raster GIS tools, saving effort, costs and resources. With the image processing tools in IDRISI, you can perform image restoration and enhancement, image transformation, image segmentation, image classification including hard and soft classifiers, accuracy assessment, and much, much more.
For over 25 years, Clark Labs has been involved in the development of geospatial technologies, supplying the community with the most comprehensive GIS and image processing system on the market.
Image Processing tools in IDRISI include:
A complete set of geometric and radiometric tools for correcting image registration and removing image distortion.
- Atmospheric correction using either the Dark Object Subtraction model, Chavez's Cos(t) model, the full radiative transfer equation model, or the Apparent Reflectance Model (ARM).
- Image mosaicking for color balancing multiple scenes into a single image.
- Radiance calibration to convert raw DN.
- Principal components analysis including standardized and unstandardized modes.
- An interactive image resampling facility.
Tools for display image enhancement that highlights information within the image.
- Image filtering that includes 10 pre-defined filters and the ability to employ a variable sized user-defined filter.
- Panchromatic merge for image pansharpening of high resolution satellite imagery.
An extensive base-image transformation tool set.
- Principal components analysis, including standardized and unstandardized, cross-product and normalized cross-product modes.
- Canonical correlation analysis for between image series pattern analysis.
- Minimum noise fraction for variable image noise reduction
- Vegetation index analysis with 19 slope-based and distance-based indices
The most extensive set of image classifiers in the industry, including hard and soft classifiers.
- Supervised classifiers that include parallelepiped, minimum distance, maximum likelihood, Fisher LDA, and k-nearest neighbor (KNN).
- Unsupervised classifiers that include ISODATA, chain cluster, and K-means.
- Machine-learning classifiers that include classification tree analysis and 4 neural network classifiers: multi-layer perceptron (MLP), Self-Organizing Map (SOM), Fuzzy ARTMAP, and Radial Basis Function.
- Soft classifiers and mixture analysis tools that include a Bayesian probability classifier, Mahalanobis distance classifier, fuzzy set classifier, linear spectral unmixing. MLP, KNN and SOM also produce soft classification outputs.
Tools for image segmentation and object-oriented classification.
- A segmentation tool that produces segments based on spectral similarity.
- An interactive signature development tool to facilitate signatures from segments.
- A majority-rule classifier specifically built for segmentation classification.
Other Key Image Processing Features in IDRISI
- Hyperspectral image analysis and classification
- Classification accuracy assessment
- Land Change Modeler, an application for the monitoring and prediction of land cover change
- Earth Trends Modeler, an application for the analysis of image time series
- A seamless link to IDRISI GIS analysis tools
- Comprehensive tutorials with data
- Import support for all the popular data archive formats
Learn More about IDRISI Selva
IDRISI Selva Brochure
IDRISI Selva Technical Specifications
Land Cover Mapping