IDCubePro 2026 Quick Start Guide | Import, Visualize & Analyze Hyperspectral Data
IDCubePro 2026 · Documentation

Quick Start: Your First Hyperspectral Analysis

Start with the IDCubePro workspace, open a dataset, explore hyperspectral images and spectra, and save your first results. Then discover advanced analysis options.

Windows & macOS Free and Pro Illustrated walkthrough
← Installation & Getting Started Windows installation Mac installation Licensing
Step 1 · Start here

Launch IDCubePro 2026 and explore the main workspace

Start IDCubePro 2026 from your desktop or applications menu. The main window is your starting point for opening data, visualizing images and spectra, accessing analysis tools, and saving your results.

IDCubePro 2026 main software interface immediately after startup, showing the toolbar, left tools panel, image display, spectrum panel and right inspector.
The IDCubePro 2026 workspace at startup. Start with the Open button in the upper-left toolbar to load your first dataset.

The upper toolbar provides access to Open , Import , Save , processing and analysis functions. The central area displays the image, the lower panel displays spectral information, and the right inspector controls the image display.

Free and Pro use the same interface. This screenshot shows a licensed Pro installation. In the Free edition, some advanced controls appear disabled or unavailable. You can still follow the introductory workflow using the features provided in your edition. To unlock Pro, see License & Activation .
Before you start

Download an example dataset

Follow along with a sample hyperspectral cube. The examples used in the original guide include multispectral and satellite imagery.

Browse example datasets ↗

File formats: A standard IDCube .mat dataset stores the 3D image and its wavelengths together. ENVI datasets typically use associated header and data files. Choose an example format supported by the importer.
Step 2

Open or import a dataset

In IDCubePro, open the data-loading interface. Choose Open IDCube Dataset for a standard .mat IDCube file. For other scientific image formats, use the Import Center and select the appropriate source format.

Open / Combine IDCube Datasets: load a standard IDCube MAT dataset.
Open / Combine IDCube Datasets: load a standard IDCube MAT dataset.
Import Center: choose from hyperspectral, remote-sensing, biomedical and other supported formats.
Import Center: choose from hyperspectral, remote-sensing, biomedical and other supported formats.
Step 3

Explore images and real-time spectra

After loading an image cube, move the pointer over the image to inspect spectral information in the spectral plot. For regions of interest, use the multi-spectra tools to compare spectra from multiple areas.

A loaded image in the main IDCubePro workspace. The example screenshot includes a data-viewer message.
A loaded image in the main IDCubePro workspace. The example screenshot includes a data-viewer message.
Real-time spectrum visualization beneath the hyperspectral image.
Real-time spectrum visualization beneath the hyperspectral image.
Multiple spectra from selected regions of interest, plotted with mean and standard deviation.
Multiple spectra from selected regions of interest, plotted with mean and standard deviation.
Step 4

Change the image display

Use the image tools to rotate or flip the view, or to crop the dataset spatially or spectrally. Adjust the spectral channels to make an RGB composite, or select a single wavelength for monochrome display.

Image menu with rotation, flip and related operations.
Image menu with rotation, flip and related operations.
Crop / Filtering / Advanced controls.
Crop / Filtering / Advanced controls.
Spectral filtering dialog for wavelength selection.
Spectral filtering dialog for wavelength selection.
RGB composite with independently adjustable red, green and blue channels.
RGB composite with independently adjustable red, green and blue channels.
Single-band display at one selected wavelength.
Single-band display at one selected wavelength.
Step 5

Measure an image region

Open Measurements , apply the appropriate spatial calibration, and choose a line, rectangle, ellipse, polygon or freehand region of interest. Measurements depend on the calibration and selection geometry.

Measurements dialog showing spatial calibration and ROI drawing options.
Measurements dialog showing spatial calibration and ROI drawing options.
Step 6

Add annotations

Use Annotation to place text labels, arrow labels or point markers on the image. Annotations help identify areas of interest for a report or presentation.

Image Annotation dialog with text, arrow, and point-label tools.
Image Annotation dialog with text, arrow, and point-label tools.
Step 7

Save and export your results

Open Save / Export Data . You can save an IDCube dataset as .mat , export a displayed image or spectra, or select supported formats such as TIFF stacks, ENVI, AVI and Excel cube tables. Available options depend on the data and workflow.

Save / Export Data menu with dataset, image, spectra and other export choices.
Save / Export Data menu with dataset, image, spectra and other export choices.
Recommended first export: save the source cube in IDCube format and export one displayed image or spectrum to verify that your workflow is reproducible.
Beyond the first steps

Explore advanced analysis

Your source guide also demonstrates PCA, clustering, AI / machine-learning tools and custom Python analysis. These are optional next steps; feature availability may depend on your IDCubePro edition and installed Python packages.

PCA and clustering

Use PCA to examine spectral variation and explore unsupervised groups in your data.

PCA explorer: component composite, spectra, explained variance, and clustering displays.
PCA explorer: component composite, spectra, explained variance, and clustering displays.

Machine learning

Open the AI / Machine Learning launcher and choose a relevant tool. The example shows a 10-cluster k-means result.

AI / Machine Learning launcher with k-means and other analysis options.
AI / Machine Learning launcher with k-means and other analysis options.
Example k-means classification with ten color-coded clusters.
Example k-means classification with ten color-coded clusters.

Custom Python analysis

The Python Analysis module lets you run a script on the current dataset. In the illustrated example, an NDWI-based workflow identifies water in satellite imagery. Python dependencies may need to be installed separately.

Custom Python Analysis editor with a satellite water-detection script.
Custom Python Analysis editor with a satellite water-detection script.
Python result: water-index image with detected water regions.
Python result: water-index image with detected water regions.

What's next?

Continue with topic-specific documentation, or review your license if a feature is unavailable in the Free edition.

Licensing & activation → Installation FAQ → Documentation hub →

Screenshots supplied from the IDCubePro 2026 Quick Guide. Interface details may vary by build and operating system.