![]() ![]() For more information about supported GPU devices, see GPU Computing Requirements (Parallel Computing Toolbox). Use of a GPU requires Parallel Computing Toolbox™. If you choose to train the network, use of a CUDA capable NVIDIA™ GPU is highly recommended. To train the network, set the doTraining variable in the following code to true. The pretrained network enables you to classify the Indian Pines data set without waiting for training to complete. ValidationFrequency=100) Train the Networkīy default, the example downloads a pretrained classifier for the Indian Pines data set. The example shows how to train a CSCNN and also provides a pretrained network that you can use to perform classification. This example uses a CSCNN that learns to classify 16 types of vegetation and terrain based on the unique spectral signatures of each material. Therefore, hyperspectral images can enable the differentiation of objects that appear identical in an RGB image. Unlike color imaging, which uses only three types of sensors sensitive to the red, green, and blue portions of the visible spectrum, hyperspectral images can include dozens or hundreds of channels. Hyperspectral imaging measures the spatial and spectral features of an object at different wavelengths ranging from ultraviolet through long infrared, including the visible spectrum. 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. Since monitors work with rgb the image has to be converted at some place before display, so i think it doesnt really matter when this takes place. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. 1 Answer Sorted by: 0 Ther is the hsv2rgb function to convert an hsv image to rgb, in case you were about to convert the values yourself. ![]() This example requires the Image Processing Toolbox™ Hyperspectral Imaging Library. This example shows how to classify hyperspectral images using a custom spectral convolution neural network (CSCNN) for classification.
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