Unsupervised hyperspectral image classification

Xiaoli Jiao; Chein-I Chang

doi:10.1117/12.732614

12 September 2007 Unsupervised hyperspectral image classification

Xiaoli Jiao, Chein-I Chang

Proceedings Volume 6661, Imaging Spectrometry XII; 66610I (2007) https://doi.org/10.1117/12.732614
Event: Photonic Devices + Applications, 2007, San Diego, California, United States

Abstract

Two major issues encountered in unsupervised hyperspectral image classification are (1) how to determine the number of spectral classes in the image and (2) how to find training samples that well represent each of spectral classes without prior knowledge. A recently developed concept, Virtual dimensionality (VD) is used to estimate the number of spectral classes of interest in the image data. This paper proposes an effective algorithm to generate an appropriate training set via a recently developed Prioritized Independent Component Analysis (PICA). Two sets of hyperspectral data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Cuprite data and HYperspectral Digital Image Collection Experiment (HYDICE) data are used for experiments and performance analysis for the proposed method.

Citation Download Citation

Xiaoli Jiao and Chein-I Chang "Unsupervised hyperspectral image classification", Proc. SPIE 6661, Imaging Spectrometry XII, 66610I (12 September 2007); https://doi.org/10.1117/12.732614

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