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SVM Classification Framework: An example showing how to use the SVM classification algorithm in ENVI with the new classification framework.

Supervised Classification - Batch: An example showing how to perform supervised classification on many images using training data from one image. Softmax Classification Framework: An example showing how to use the Softmax classification algorithm in ENVI with the new classification framework. Note that, if you use this, you will want to register your images to one another if they are not already as this is a pixel-based change detection algorithm. SAM Image Difference: Uses a new ENVI Task called 'SAM Image Difference' to perform change detection. Raster Stacking: Demonstrates how to stack an array of rasters if they have overlap. The band math expression should only contain the band identifier b1 which will represent each band as the model loops through the raster. Raster Math: This tasks applies a single bandmath expression to exery band of a raster. Pan Sharpening Iterator: Shows how to use one Iterator node to process two arrays of rasters to perform NNDiffusePanSharpening on all rasters. Pan Sharpening Comparison: Compares the different PanSharpening algorithms available in ENVI This requires nested looping, an advanced feature that is achieved using metatasks. Nested Loop Example: Comparing all the bands in a raster file against themselves.

NDVI on Raster Series: Example processing every raster in a raster series. Shows how the Iterator node works in the modeler. Iterate over number of classes: Automtatically run ISODATA classification with 3,6, and 9 classes and visualize the results. Image Change with Spectral Index: This model follows the Image Change workflow and uses a spectral index as the metric to quantify change. Hillshade Image: This model takes a DEM and creates an image where color shows elevation and shading represents terrain shape. Generate Contour Lines: This simple example generates contour lines on a raster using ISODATAClassification and is an example of how you can use algorithms in ENVI for different applications. Alunite and Kaolinite are mapped in this example. Brighter pixels represent a close match to the 'Dry Grass' spectrum in the veg_2grn.sli spectral library.ДЄutomatic Image Registration: Simple model that demonstrates how to perform image-image registration.Д¬omparing Classification Methods: Nice model that compares four different classification methods on a single raster and ENVI ROI.Дark Subtraction with Mask Support: Example model that performs dark subtraction on a raster and supports masked pixels.ДЇind Minerals: Example that demonstrates how to map minerals in a hyperspectral image using ENVI's hyperspectral tools. Actually it runs ACE target detection analysis, using the mean and covariance from subspace background statistics. Here is a list and short description of the included models:ДЄCE Target Detection: This is a map of the McCaslin and US-36 interchange. In order to run these models, you will need ENVI 5.5 or newer. However, it is easy to replace/update the input data nodes with your own data to try them on your own. Note that you will not be able to run every model that is included because you will not have the data.

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ENVI integrates with your existing tools making it easy to update your GIS and collaborate and share results with others.