Classification non supervis{é}es d'acquisitions hyperspectrales cod{é}es : quelles v{é}rit{é}s terrain ?
This addresses evaluation challenges for unsupervised hyperspectral classification methods, though it appears incremental in scope.
The authors tackled unsupervised classification of compressed hyperspectral data, showing their method can identify classes and estimate reference spectra despite 10x data compression while highlighting limitations in commonly used ground truth evaluations.
We propose an unsupervised classification method using a limited number of coded acquisitions from a DD-CASSI hyperspectral imager. Based on a simple model of intra-class spectral variability, this approach allow to identify classes and estimate reference spectra, despite data compression by a factor of ten. Here, we highlight the limitations of the ground truths commonly used to evaluate this type of method: lack of a clear definition of the notion of class, high intra-class variability, and even classification errors. Using the Pavia University scene, we show that with simple assumptions, it is possible to detect regions that are spectrally more coherent, highlighting the need to rethink the evaluation of classification methods, particularly in unsupervised scenarios.