Spectral Information Divergence Map

Features:

• Interactively selects a seed.

• Show a divergence between the seed and the rest of the image.

• Identifies the area of the highest/lowest divergence.

• Quantifies the area correlated with the seed.

Steps:

1.       Press the button Spectral Mathematics under Spectral Analysis on the Main Interface.

2.       Select Spectral Divergence Map from a dropdown menu.

3.       Select 1st spectrum.

4.       Draw a region of interest and visualize the mean spectrum.

5.       Click Generate to open another window Spectral Divergence Map. The mapping calculations will start.

6.       Visualize and quantify a spectral information divergence map.

Lower divergence corresponds to a closer match between the seed and each pixel in the image. Use a slider on the histogram to visualize the images with the highest or lowest correlation. The total area of the matched divergence (low values) is shown in the title. 

Using the Reset button below the image, revert the image to the original.

Note: For this calculation, only values between the cyan line and the left edge of the histogram are counted.

Additional Information:

Spectral Information Divergence (SID) measures the spectral similarity between the specified test spectra (pixels in the image) and reference spectra (mean spectrum) from the selected ROI by using the SID method. This method is used to compare the spectral signature of an unknown material against the seed reference spectra or to compute spectral variability between two spectral signatures.

SID: measures the spectral similarity between the spectra of each pixel in the hyperspectral data and the specified reference by using the spectral information divergence (SID) technique. The method computes spectral similarity based on the divergence between the probability distributions of the two spectra.

References:

SID: Chein-I Chang. “An Information-Theoretic Approach to Spectral Variability, Similarity, and Discrimination for Hyperspectral Image Analysis.” IEEE Transactions on Information Theory 46, no. 5 (August 2000): 1927–32. https://doi.org/10.1109/18.857802.