Solving Classification Problem on Hyperspectral Images
Talor Abramovich and Oz Gavrielov
Supervised by Amir Adler
Hyperspectral Imaging is a spectral imaging method, which includes bands from the visible light as well as infra red. Unlike the 2D color images, which only use red, green and blue, hyperspectral image includes a third dimension of spectrum. This information can be used to classify the objects in the image, and to define the difference between asphalt, plants and water. It could even show the difference between real leaf and a plastic one. In our project we used several classification algorithms, including KNN, PCA and KSVD to classify four hyperspectral images. We compared the results and found which algorithm gives the best classification and which is the most efficient.
Please, see project report.
Please, see final presentation.