On the use of a hybrid approach to contrast endmember induction algorithms

Authors:
Miguel A. Veganzones;Carmen Hernández
Affiliations:
Computational Intelligence Group, UPV/EHU, Facultad Informatica, Paseo Manuel de Lardizabal, San Sebastian, Spain;Computational Intelligence Group, UPV/EHU, Facultad Informatica, Paseo Manuel de Lardizabal, San Sebastian, Spain
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Year:
2010

Citing 2
Cited 1

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Two lattice computing approaches for the unsupervised segmentation of hyperspectral images

Neurocomputing

An endmember-based distance for content based hyperspectral image retrieval

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In remote sensing hyperspectral image processing, identifying the constituent spectra (endmembers) of the materials in the image is a key procedure for further analysis The contrast between Endmember Inductions Algorithms (EIAs) is a delicate issue, because there is a shortage of validation images with accurate ground truth information, and the induced endmembers may not correspond to any know material, because of illumination and atmospheric effects In this paper we propose a hybrid validation method, composed on a simulation module which generates the validation images from stochastic models and evaluates the EIA through Content Based Image Retrieval (CBIR) on the database of simulated hyperspectral images We demonstrate the approach with two EIA selected from the literature.