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.
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.
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