ENVI for Hyperspectral Image Processing Training Course

Course overview

Hyperspectral imagery can distinguish minerals, vegetation stress, water constituents and man-made materials that multispectral imagery cannot reliably separate. Yet raw hyperspectral cubes contain hundreds of bands, sensor artefacts, atmospheric effects and mixed pixels that can quickly produce misleading maps if processing choices are not traceable. This course equips GIS and remote-sensing professionals to turn airborne, satellite or drone-derived hyperspectral data into defensible classification, target-detection and material-mapping outputs using ENVI.

Participants work through the ENVI workflow from data import and metadata inspection to radiometric calibration, atmospheric correction, bad-band removal, noise reduction and dimensionality reduction. They learn to build and manage spectral libraries; extract endmembers; perform spectral angle mapper, matched filtering and linear spectral unmixing; train supervised classifiers; assess accuracy; and publish map-ready results. The course also addresses practical decisions such as selecting regions of interest, managing spectral variability, choosing validation samples and documenting processing parameters for repeatable analysis.

Delivery combines instructor demonstrations with guided ENVI labs using representative hyperspectral datasets from environmental and land-cover applications. Each participant completes a structured processing workflow and produces a documented mini-project: a corrected hyperspectral dataset, spectral signatures, classified or target-detection map, accuracy assessment and an ENVI Modeler workflow that can be rerun on comparable data. This gives both the attendee and their organisation a practical template for moving from exploratory analysis to operational image-processing tasks.

The course is suited to professionals who already work with geospatial or earth-observation data and need a focused route into hyperspectral analysis. It is equally valuable for teams evaluating whether hyperspectral acquisition can support mineral exploration, precision agriculture, environmental monitoring, coastal analysis or infrastructure-related material identification.

Course objectives

By the end of this course, participants will be able to:

• Inspect hyperspectral cube metadata, wavelength calibration, interleave formats and data-quality indicators in ENVI
• Apply radiometric calibration, bad-band removal and atmospheric correction to prepare analysis-ready imagery
• Reduce spectral noise and data dimensionality using MNF transforms and pixel-purity analysis
• Build regions of interest and spectral libraries from image spectra, field spectra and reference materials
• Extract endmembers and compare spectral signatures using continuum removal and spectral similarity measures
• Run Spectral Angle Mapper, matched filtering and linear spectral unmixing for material identification
• Train supervised land-cover classifiers and produce confusion matrices, accuracy metrics and error maps
• Automate a repeatable hyperspectral workflow in ENVI Modeler and export map-ready outputs

Benefits of attending

Target competencies

Who should attend

• Remote Sensing Analysts — who need to convert hyperspectral scenes into validated thematic and material maps
• GIS Analysts — who integrate earth-observation outputs into spatial databases, dashboards and decision products
• Environmental Scientists — who assess vegetation condition, water quality, contamination or habitat characteristics
• Geologists and Mineral Exploration Teams — who use diagnostic spectral features to identify alteration minerals and lithology
• Precision Agriculture Specialists — who need earlier, spatially detailed indicators of crop stress and field variability
• Earth Observation Project Managers — who must assess hyperspectral processing capability, quality controls and delivery risk

Requirements and prerequisites

Participants should be comfortable working with raster datasets in a GIS or remote-sensing environment and understand basic concepts including pixels, coordinate reference systems, spectral bands, reflectance and supervised classification. Prior experience with ENVI is helpful but not required; the course introduces its interface, layer management and processing tools from first use. Participants should be able to interpret simple charts and tabular accuracy results. Programming, command-line GDAL use, advanced statistics and prior hyperspectral project experience are not required. A complete beginner to GIS should first gain basic raster and coordinate-system familiarity.

Training methodology

The week alternates short instructor-led explanations with guided ENVI processing labs. Participants inspect real hyperspectral cubes, make parameter choices in the ENVI toolbox, and compare outputs from alternative correction, transformation and classification methods. Case discussions examine how spectral variability, atmospheric conditions and inadequate validation affect project conclusions. Small-group reviews are used to challenge class definitions and accuracy evidence. On the final day, each participant assembles an ENVI Modeler workflow and application plan for a relevant workplace dataset or use case.

Course outline

Day 1: Hyperspectral data and ENVI foundations

• Hyperspectral sensor types, spectral resolution and application constraints
• ENVI interface, layer manager and data manager navigation
• Raster interleave formats, wavelength metadata and header-file inspection
• Radiance, reflectance and digital-number data interpretation
• Band statistics, histograms and interactive spectral profiles
• Spatial reference checks, subsetting and raster reprojection
• Data-quality review for noisy bands, saturation and missing values

Workshop: Import and inspect a hyperspectral scene in ENVI, producing a data-quality checklist, subset image and spectral-profile comparison.

Day 2: Preprocessing and spectral enhancement

• Radiometric calibration from raw sensor values to radiance or reflectance
• FLAASH atmospheric correction parameters and output review
• QUAC atmospheric correction for rapid scene-based processing
• Bad-band identification and removal using spectral plots
• Destriping, noise reduction and spatial-spectral filtering
• Minimum Noise Fraction transformation and eigenvalue interpretation
• Pixel Purity Index for locating spectrally pure candidates

Workshop: Process a raw or radiance hyperspectral cube through correction, bad-band removal and MNF transformation, then document the chosen parameters.

Day 3: Spectral libraries and material identification

• Region of interest creation from imagery and reference polygons
• Spectral Library Builder and spectral library metadata management
• Image spectra, field spectra and laboratory reference spectra comparison
• Continuum removal for diagnostic absorption-feature analysis
• Spectral Angle Mapper thresholds and rule rasters
• Matched filtering and adaptive coherence estimation concepts
• Linear spectral unmixing for mixed-pixel abundance estimates

Workshop: Build a project spectral library and generate a material-target map using Spectral Angle Mapper and matched filtering.

Day 4: Classification and accuracy assessment

• Class schema design for land cover and material-mapping projects
• Training and validation sample selection strategies
• Maximum likelihood and support vector machine classification in ENVI
• Feature selection using bands, MNF components and spectral indices
• Post-classification filtering and class aggregation
• Confusion matrices, producer accuracy and user accuracy
• Kappa, F1 score and spatial review of classification errors

Workshop: Train and validate a supervised classifier, producing a thematic map, confusion matrix and short interpretation of major error sources.

Day 5: Operational workflows and project delivery

• ENVI Modeler workflow design and parameter linking
• Batch processing for multiple scenes and repeatable outputs
• Exporting GeoTIFF, vector results and classified rasters
• Publishing outputs for use in ArcGIS Pro mapping projects
• Processing provenance, metadata and reproducibility records
• Quality assurance checks for operational hyperspectral deliverables
• Use-case planning for mineral, vegetation, water and land-cover applications

Workshop: Create an ENVI Modeler workflow and final project pack containing processed imagery, a thematic output, validation evidence and a workplace application plan.

Tools & standards covered

ENVI, ENVI Modeler, ENVI Spectral Library Builder, ArcGIS Pro

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions