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HYPERNET

HYPERNET is a library which implements state-of-the-art and new algorithms for (among others):

• accurate hyperspectral image (HSI) segmentation and analysis using deep neural networks,
• optimization of deep neural network architectures for hyperspectral data segmentation,
• hyperspectral data augmentation,
• validation of existent and emerging HSI segmentation algorithms,
• simulation of multispectral data using HSI.

BEETLES

HYPERNET is a project that is a follow-up of HYPERNET, and expands our battery of algorithms in the following (this list will be updated):

• generating noisy test data by injecting simulated noise of a given distribution (e.g., Gaussian, impulsive, Poisson),
• quantization and DPU compilation of the deep neural networks, e.g., with the use of the Xillinx DNNDK tool,
• deep learning-powered hyperspectral unmixing.

Requirements

The main requirements in python 3.6, available from https://www.python.org/downloads/

GUI application uses QT5, it can be downloaded from https://www.qt.io/download-qt-installer

All other requirements are listed in requirements.txt and they can be installed by running pip install -r requirements.txt