The test data contains nine endmembers that represent these ground truth classes: Asphalt, Meadows, Gravel, Trees, Painted metal sheets, Bare soil, Bitumen, Self blocking bricks, and Shadows. This example uses a data sample from the Pavia University dataset as test data. The Image Processing Toolbox Hyperspectral Imaging Library requires desktop MATLAB®, as MATLAB® Online™ and MATLAB® Mobile™ do not support the library. (We can't tell you this, but its normally 8 bit for a standard RGB image, but can be as high as 24 or as low as 4, for whatever it is that you're looking at.) Once you know that: totalnumberofcolours 2depthslice1 2depthslice2 2depthslice3. For more information about installing add-ons, see Get and Manage Add-Ons. You need to know the bit depth of each 'slice' of your hypercube. This syntax is the low-level version of image (C). image ('CData',C) adds the image to the current axes without replacing existing plots. The image is stretched and oriented as applicable. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. To specify the first corner and let image determine the other, set x and y as scalar values. This example requires the Image Processing Toolbox™ Hyperspectral Imaging Library. In this example, you will classify the pixels in a hyperspectral image by finding the maximum abundance value for each pixel and assigning it to the associated endmember class. The set of abundance values obtained for each pixel represents the percentage of each endmembers present in that pixel. Each pixel in the image is either a pure pixel or a mixed pixel. You may determine the menu order for metadata, groups, texts and light tables as well as set the visibility for individual groups, texts or light tables. An abundance map characterizes the distribution of an endmember across a hyperspectral image. The HyperImage Publication Tool opens in a web browser and allows you to select the options for the static publication of your project. This example shows how to identify different regions in a hyperspectral image by performing maximum abundance classification (MAC).
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