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Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information
Lee, Chulhee (Yŏnse Taehakkyo. School of Electrical and Electronic Engineering)
Youn, Sungwook (Yŏnse Taehakkyo. School of Electrical and Electronic Engineering)
Jeong, Taeuk (Yŏnse Taehakkyo. School of Electrical and Electronic Engineering)
Lee, Eunjae (Yŏnse Taehakkyo. School of Electrical and Electronic Engineering)
Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Data: 2015
Resum: It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.
Nota: Número d'acord de subvenció MINECO/TIN2012-38102-C03-03
Nota: Número d'acord de subvenció AGAUR/2014/SGR-691
Drets: Tots els drets reservats
Llengua: Anglès
Document: article ; recerca ; acceptedVersion
Publicat a: IEEE geoscience and remote sensing letters, Vol. 12 Núm. 7 (July 2015) , p. 1491-1495, ISSN 1545-598X

DOI: 10.1109/LGRS.2015.2409897

5 p, 1.6 MB

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