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| ENVI (Environment for Visualizing Images) | |
|---|---|
| Name | ENVI (Environment for Visualizing Images) |
| Developer | Harris Geospatial Solutions |
| Released | 1990s |
| Latest release | Commercial releases (continuous updates) |
| Operating system | Microsoft Windows |
| Genre | Remote sensing, image analysis, geospatial |
| License | Proprietary |
ENVI (Environment for Visualizing Images) is a commercial software suite for remote sensing and geospatial image analysis widely used in industry and academia. It integrates tools for multispectral, hyperspectral, and radar processing with visualization components tailored for users in NASA, European Space Agency, United States Geological Survey, National Oceanic and Atmospheric Administration and corporate settings like Lockheed Martin, Raytheon Technologies, Airbus, and Maxar Technologies. The package interoperates with common geospatial platforms and standards from organizations such as Open Geospatial Consortium, ISO and American Society for Photogrammetry and Remote Sensing.
ENVI provides graphical and programmatic environments for processing raster imagery from sensors produced by vendors like DigitalGlobe, Planet Labs, Sentinel-2, Landsat, SPOT, IKONOS, WorldView, and science missions by NASA and ESA. The interface connects visualization modules with analytical toolboxes used in projects involving United Nations, World Bank, European Commission, US Department of Defense, and research institutions including Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, University of Oxford, and Harvard University. ENVI is frequently paired with GIS platforms such as Esri's ArcGIS and programming ecosystems like Python (programming language), IDL (programming language), and MATLAB.
Development began in the early 1990s within companies and research groups engaged with remote sensing technology influenced by programs like Landsat Program, SPOT Program, and projects at Jet Propulsion Laboratory. The software evolved through corporate changes involving ITT Corporation and later acquisitions by Exelis Inc. and Harris Corporation before formation of Harris Geospatial Solutions. ENVI’s roadmap reflected advances driven by initiatives such as NASA Earth Observing System, Copernicus Programme, and collaborations with academic centers like California Institute of Technology, University of Maryland, and Purdue University.
ENVI’s architecture rests on a graphical user interface with an underlying processing engine exposed via scripting APIs for IDL (programming language) and Python (programming language). Core features include radiometric and geometric correction tools used in workflows for Landsat, MODIS, Sentinel-1, and Sentinel-2 data, pan-sharpening compatible with WorldView collections, and spectral analysis tuned to datasets from AVIRIS, Hyperion, and airborne sensors by National Center for Airborne Laser Mapping. The platform supports integration with database technologies from Oracle Corporation, Microsoft, and cloud providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Visualization capabilities align with standards set by Open Geospatial Consortium and mapping conventions in Esri products.
ENVI handles a range of raster and ancillary formats including standards from GeoTIFF specifications, HDF5 containers used by NASA missions, and NetCDF files common in climate science by NOAA and European Centre for Medium-Range Weather Forecasts. It supports proprietary collections distributed by Maxar Technologies, Airbus, and Planet Labs as well as airborne lidar formats associated with USGS and academic consortia like NEON. Interoperability extends to vector and metadata standards promoted by Open Geospatial Consortium and cataloging systems used by Copernicus and national mapping agencies such as Ordnance Survey and National Geospatial-Intelligence Agency.
ENVI bundles algorithms for classification, change detection, and spectral unmixing drawn from literature and implemented to process datasets from instruments including AVIRIS, MODIS, Sentinel-2, and Landsat 8. Tools implement common methods like support vector machines, random forests, and principal component analysis—algorithms often compared in studies at institutions such as University of California, Santa Barbara, Carnegie Mellon University, and University of Minnesota. Specialized modules address hyperspectral techniques (continuum removal, SAM, matched filtering) and synthetic aperture radar processing (speckle filtering, polarimetric decomposition) relevant to European Space Agency and Japan Aerospace Exploration Agency missions. Workflow automation uses APIs compatible with Python (programming language) packages developed by communities around NumPy, SciPy, and scikit-learn.
ENVI is applied across domains by agencies like US Geological Survey, NASA, NOAA, Defense Advanced Research Projects Agency, and companies including Booz Allen Hamilton, Accenture, and Siemens. Typical use cases include land cover mapping for projects funded by World Bank, disaster assessment after events cataloged by United Nations Office for the Coordination of Humanitarian Affairs, agriculture monitoring supporting Food and Agriculture Organization, mineral exploration in collaborations with firms like Rio Tinto and BHP, coastal change tracking for National Oceanic and Atmospheric Administration, and defense intelligence workflows with NGA and US Department of Defense components.
ENVI is distributed commercially by Harris Geospatial Solutions under proprietary licensing models tailored to enterprise, academic, and government customers. Licensing tiers and maintenance agreements reflect procurement practices used by organizations such as NASA, US Department of Defense, European Commission, and multinational corporations including Airbus and Lockheed Martin. Academic programs and consortia at University of Wisconsin–Madison, Oregon State University, and others often obtain campus-wide licenses for instructional use.
Critics in academic and open-source communities point to ENVI’s proprietary licensing compared to open projects like QGIS, GRASS GIS, Orfeo Toolbox, and libraries from OpenCV and GDAL. Concerns raised by researchers at Stanford University, MIT, and University of Cambridge emphasize reproducibility, transparency of algorithm implementations, and interoperability with emerging cloud-native workflows from Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Additional limitations cited by practitioners in agencies such as USGS and NOAA include platform dependence on Microsoft Windows and integration challenges with modern containerization systems used in projects at Lawrence Berkeley National Laboratory and Argonne National Laboratory.
Category:Remote sensing software