The Spectral Hourglass Workflow guides you through process to automatically find and map image spectral endmembers from hyperspectral or multispectral data.
See the following for help on a particular step of the workflow:
Workflow Tips
This workflow is not “modal,” meaning it will not block you from using other ENVI tools or working with additional data. This is useful in that the workflow will not prevent you from doing multiple things at a time. However, be aware that if you close all of your files in the middle of the workflow process, you might not be able to continue the workflow and will need to start over.
Navigating Workflow Steps
The number of steps provided in the workflow will depend on the input image data. For example, not all images will contain the data needed for every step; therefore, some steps will be skipped automatically.
Some steps can be optional; in those cases, the Perform this step radio button is selected by default. To skip that step and go to the next step in the workflow, select the Skip this step radio button, then click Next.
The timeline at the bottom of the workflow will display the order of steps available for the workflow and your data, and the title of your current location in the workflow will flash. The title is also an active link that you can click, to jump backward or forward to a desired step in the workflow.
Preview/Display Result
Some workflow steps provide options to preview the settings and/or to display the processed result.
- Enable the Preview check box to see a preview of the settings before you click OK and process the data. The preview is calculated only on the area in the view and uses the resolution level at which you are viewing the image. See Preview for details on the results. To preview a different area in your image, pan and zoom to the area of interest and re-enable the Preview option.
- Enable the Display result check box to display the raster in the view when processing is complete.
Open Workflow in Modeler
On the last step of the workflow, the Open Workflow in Modeler link will take your full workflow - the exact data, choices, and parameter values that you selected - and create a Model that can be manipulated in the ENVI Modeler. For example, you could create a Model to perform batch processing with multiple similar input datasets.
Select Data
- From the Toolbox, select Workflows > Spectral Hourglass Workflow. The Select Data panel appears.
- Select an input file and perform optional spatial and spectral subsetting and/or masking, then click OK.
- Click Next. An MNF transform is performed on the raster, which determines the inherent dimensionality of image data, segregates and equalizes the noise in the data, and reduces the computational requirements for subsequent processing.
MNF Dimensionality Reduction
After the MNF transform is done, the MNF Dimensionality Reduction panel appears.
The Explained Variance plot shows the sorted eigenvalue (or spatial coherence) contribution percentage of each band after image transform. Use the plot as a guidance for choosing number of bands to keep. For example, you have a 50-band image and the plot indicates contributions from the first 14 bands contribute over 90% of the total variance. If this amount is suitable, you can use 14 as the number of bands to keep. The analysis after this step will use the first 14 bands of transformed image instead of all 50 bands, reducing dimensionality from 50 to 14.
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Enter the Reduced Number of Bands.
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Click the View MNF Raster button to optionally view the MNF output raster. The raster is added to the View.
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Click Next.
Pixel Purity Index
The Pixel Purity Index panel appears. PPI will take the most important MNF bands and find the most spectrally pure (extreme) pixels. These typically correspond to endmembers. The PPI is computed by repeatedly projecting n-D scatter plots on a random unit vector. ENVI records the pure pixels in each projection (those pixels that fall onto the ends of the unit vector) and it notes the total number of times each pixel is marked as pure. A Pixel Purity Image is created where each pixel value corresponds to the number of times that pixel was recorded as pure.
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Enter the number of Pixel Purity Index Iterations to perform. With more iterations, ENVI does a better job of finding the pure pixels.
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Enter a Threshold Factor value in data units for pure pixel selection. This threshold selects the pixels on the ends of the projected vector. For example, a value of 2 will flag all pixels greater than two DN values from the extreme pixels (both high and low) as extreme. The threshold should be approximately two to three times the noise level in the data. Landsat TM data, for example, typically have less than 1 DN of noise, so a threshold value of 2 or 3 works well. When using MNF data, which normalizes the noise, a DN is equivalent to one sigma, so a threshold value of 2 or 3 works well. Larger thresholds cause the PPI to find more extreme pixels, but they are less likely to be pure endmembers.
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Click Next.
N-Dimensional Visualizer
The N-Dimensional Visualizer panel and window appear. PPI identified all the pure pixels in the image; in this step, you will group them into spectrally similar clusters, each representing a different endmember. The pixels with the highest PPI value are loaded as a pre-clustered result in the N-Dimensional Visualizer. This gives you a starting point for interactively rotating and refining the clusters of pixels into class groups.
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Set the Max. PPI Pixels to Use in n-D Visualizer.
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See the N-Dimensional Visualizer topic for details on how to use it.
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When you are finished using the N-Dimensional Visualizer, click Next.
Calculate Abundances
The Calculate Abundances panel appears. This step calculates how much of each endmember is present on each pixel (abundance) or what endmember is the biggest contributor to a pixel’s spectral signature (classification).
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Select a classification Method to use:
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Mixture Tuned Matched Filter: (default) Mixture Tuned Matched Filter (MTMF) supervised classification.
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Spectral Angle Mapper Classification: Spectral Angle Mapper (SAM) supervised classification. SAM is a physically-based spectral classification that uses an n-D angle to match pixels to reference spectra.
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Linear Spectral Unmixing: Linear Spectral Unmixing determines the relative abundance of materials that are depicted in the raster based on the endmembers' spectral signatures.
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Click Next.
Review
The Review Results panel appears and output layers for the selected classification method are added to the Layer Manager.
Review Mixture Tuned Matched Filter Results
The output from SAM is a classified image and a set of rule images (one per endmember).
Choose a class from the Select drop-down list. You can do the following for each class:
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To load the rule image for the selected band, click the View button.
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To use a 2-D Scatter Plot to compare the MF score to the infeasibility score, click the Infeasibility button.
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To plot the endmember spectra, click the Plot Endmembers button.
Review Spectral Angle Mapper Classification Results
The output from SAM is a classified image and a set of rule images (one per endmember). The pixel values of the rule images represent the spectral angle in radians from the reference spectrum for each class. Lower spectral angles represent better matches to the endmember spectra. Areas that satisfied the selected radian threshold criteria are carried over as classified areas into the classified image.
Choose a class from the Select drop-down list. You can do the following for each class:
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To load the rule image for the selected band, click the View button.
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View the classification image, click the View SAM Classification button.
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To plot the endmember spectra, click the Plot Endmembers button.
Review Linear Spectral Unmixing Results
The output from Linear Spectral Unmixing is a classified image and a set of rule images (one per endmember).
Choose a class from the Select drop-down list. You can do the following for each class:
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To load the rule image for the selected band, click the View button.
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To view the Lindear Spectral Unmixing Error image, click the View Unmix RMS Error button.
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To plot the endmember spectra, click the Plot Endmembers button.
When you are finished reviewing the results, click Next.
Export
The Export panel appears. Select the products to create.
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Enable check boxes for the desired exports. A Filename field will appear for the exports you enable; enter a filename and location for each. The options are:
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Click Finish.