Use the n-D Visualizer to locate, identify, and cluster the purest pixels and the most extreme spectral responses (endmembers) in a dataset in n-dimensional space. The n-D Visualizer was designed to help you visualize the shape of a data cloud that results from plotting image data in spectral space (with image bands as plot axes). You typically use the n-D Visualizer with spatially subsetted Minimum Noise Fraction (MNF) data that use only the purest pixels determined from the Pixel Purity Index (PPI).

When using the n-D Visualizer, you can interactively rotate data in n-D space, select groups of pixels into classes, and collapse classes to make additional class selections easier. You can export the selected classes to ROIs and use them as input into classification, Linear Spectral Unmixing, or Matched Filtering techniques.

Note: See Spectral Hourglass Wizard for instructions on the ENVI hourglass processing flow, including the n-D Visualizer, to find and map image spectral endmembers from hyperspectral or multispectral data.

See the following sections: