Howtosignalprocessing
- What is a limitation of hyperspectral images?
- What is the biggest hurdle in working with hyperspectral imagery?
- How do you process hyperspectral images?
- What is hyperspectral image classification?
What is a limitation of hyperspectral images?
The primary disadvantages are cost and complexity. Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data.
What is the biggest hurdle in working with hyperspectral imagery?
Mixed-pixel interference is one of the main obstacles in hyperspectral imagery.
How do you process hyperspectral images?
For hyperspectral image processing, the values read from the data file are arranged into a three-dimensional (3-D) array of form M-by-N-by-C, where M and N are the spatial dimensions of the acquired data, C is the spectral dimension specifying the number of spectral wavelengths used during acquisition.
What is hyperspectral image classification?
Hyperspectral image classification is the task of classifying a class label to every pixel in an image that was captured using (hyper)spectral sensors. ( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )
