In this video, we explore hyperspectral image classification using a deep CNN in PyTorch. Hyperspectral imaging goes beyond standard RGB by capturing hundreds of narrow wavelength bands, giving each pixel a rich spectral signature that can reveal the material it’s made of.
Combined with AI and deep learning, this powerful data can be used for:
Agriculture – crop health and disease detection
Environment – pollution, deforestation, and water quality monitoring
Medicine – non-invasive tissue and cancer analysis
Food industry – quality control and contamination detection
I’ll walk through how a 2D CNN can learn both spectral and spatial features from hyperspectral images, explaining the key layers (convolutions, pooling, dropout, fully connected, softmax) and then implement everything in PyTorch using the classic Indian Pines dataset.
To access the code, use the following link:
https://github.com/mortezmaali/AI_HSI_PT.git
