I want to share about a topic I’ve been studying for a while **hyperspectral imaging (HSI)** . HSI captures images across many wavelengths, providing detailed spectral data for each pixel. This enables the identification of materials, objects, and conditions that are not visible in regular images.
For classification, we first extract spectral features from these images and then use machine learning models, like Support Vector Machines (SVM), Random Forests, or Convolutional Neural Networks (CNNs), to identify patterns and classify the data. The training process involves using labeled hyperspectral datasets, where the model learns the relationship between the spectral features and their corresponding labels. After training, the model can classify new images based on their spectral data.
I will share the progress of my journey learning about it i hope i will find who is interested 🤓