![]() ![]() 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. For more information about installing add-ons, see Get and Manage Add-Ons. Last post, we discussed visualizations of features learned by a neural network. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. I’m hoping by now you’ve heard that MATLAB has great visualizations, which can be helpful in deep learning to help uncover what’s going on inside your neural network. This example requires the Image Processing Toolbox™ Hyperspectral Imaging Library. This is where I find fprintf comes in handy to let us do formatted printing to the screen. 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. Visualize Data with MATLAB Click Apps > MATLAB Visualizations. So how can we visualize this data One thing we can do is print the data set to to the MATLAB command window and a more pleasant and customized format, the default format MATLAB uses to print out variables. 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. The top three predictions and confidence are displayed in the title of the plot. An abundance map characterizes the distribution of an endmember across a hyperspectral image. subplot(1,2,1) imshow(im) subplot(1,2,2) CAMshow(im,classActivationMap) title(string(labels) + ', ' + string(maxScores)) drawnow The activations for the top prediction are visualized. This example shows how to identify different regions in a hyperspectral image by performing maximum abundance classification (MAC).
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