Leaderboard

Added support for data types: GRUS L1C, L2A - Axelspace micro-earth observation satellite ISIS3 - USGS Astrogeology ISIS Cube, Version 3 PDS4 -NASA Planetary Data System, Version 4 New Spectral Hourglass Workflow and N-Dimensional Visualizer New Target Detection Workflow The Target Detection Workflow has been added to this release. Use the Target Detection Workflow to locate objects within hyperspectral or multispectral images that match the signatures of in-scene regions. The targets may be a material or mineral of interest, or man-made objects. New Dynamic Band Selection tool New Material Identification tool Updated and improved Endmember Collection tool New and updated ENVI Toolbox tools The following tools have been updated to use new ENVI Tasks: Adaptive Coherence Estimator Classification: A classification method derived from the Generalized Likelihood Ratio (GLR) approach. The ACE is invariant to relative scaling of input spectra and has a Constant False Alarm Rate (CFAR) with respect to such scaling. Constrained Energy Minimization Classification: A classification method that uses a specific constraint, CEM uses a finite impulse response (FIR) filter to pass through the desired target while minimizing its output energy resulting from a background other than the desired targets. Classification Smoothing: Removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class. Forward Minimum Noise Fraction: Performs a minimum noise fraction (MNF) transform to determine the inherent dimensionality of image data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing. Inverse Minimum Noise Fraction: Transforms the bands from a previous Forward Minimum Noise Fraction to their original data space. Orthogonal Subspace Projection Classification: This classification method first designs an orthogonal subspace projector to eliminate the response of non-targets, then Matched Filter is applied to match the desired target from the data. Parallelepiped Classification: Performs a parallelepiped supervised classification which uses a simple decision rule to classify multispectral data. Spectral Information Divergence Classification: A spectral classification method that uses a divergence measure to match pixels to reference spectra. New and updated ENVI Tasks You can use these new ENVI Tasks to perform data-processing operations in your own ENVI+IDL programs: ConstrainedEnergyMinimization: Performs the Constrained Energy Minimization (CEM) target analysis. InverseMNFTransform: Transforms the bands from a previous Forward Minimum Noise Fraction to their original data space. MixtureTunedRuleRasterClassification: Applies threshold and infeasibility values and performs classification on mixture tuned rule raster. MixtureTunedTargetConstrainedInterferenceMinimizedFilter: Performs the Mixture Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF) target analysis. NormalizedEuclideanDistanceClassification: Performs a Normalized Euclidean Distance (NED) supervised classification. OrthogonalSubspaceProjection: Performs the Orthogonal Subspace Projection (OSP) target analysis. ParallelepipedClassification: This task performs a parallelepiped supervised classification which uses a simple decision rule to classify multispectral data. RuleRasterClassification: Creates a classification raster by thresholding on each band of the raster. SpectralInformationDivergenceClassification: Performs the Spectral Information Divergence (SID) classification. SpectralSimilarityMapperClassification: Performs a Spectral Similarity Mapper (SSM) supervised classification. TargetConstrainedInterferenceMinimizedFilter: Performs the Target-Constrained Interference-Minimized Filter (TCIMF) target analysis. ENVI performance improvements NITF updates Merged ENVI Crop Science Module into ENVI Enhanced support for ENVI Connect also you may check this presentation: https://www.nv5geospatialsoftware.com/Portals/0/pdfs/envi-6.0-idl-9.0-redefining-image-analysis-webinar.pdf