Paper
5 September 2008 Interactive transmission of spectrally wavelet-transformed hyperspectral images
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Abstract
The size of images used in remote sensing scenarios has constantly increased in the last years. Remote sensing images are not only stored, but also processed and transmitted, raising the need for more resources and bandwidth. On another side, hyperspectral remote sensing images have a large number of components with a significant inter-component redundancy, which is usually taken into account by many image coding systems to improve the coding performance. The main approaches used to decorrelate the spectral dimension are the Karhunen Loeve-Transform and the Discrete Wavelet Transform (DWT). This paper is focused on DWT decorrelators because they have a lower computational complexity, and because they provide interesting features such as component and resolution scalability and progressive transmission. Influence of the spectral transform is investigated, considering the DWT kernel applied and the number of decomposition levels. In addition, a JPIP compliant application, CADI, is introduced. It may be useful to test new protocols, techniques, or coding systems, without requiring significant changes on the application. CADI can be run in most computer platforms and devices thanks to the use of JAVA and the configuration of a light-version, suitable for devices with constrained resources.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
José Lino Monteagudo-Pereira, Joan Bartrina-Rapesta, Francesc Aulí-Llinàs, Joan Serra-Sagristà, Alaitz Zabala, and Xavier Pons "Interactive transmission of spectrally wavelet-transformed hyperspectral images", Proc. SPIE 7084, Satellite Data Compression, Communication, and Processing IV, 708405 (5 September 2008); https://doi.org/10.1117/12.794749
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Cited by 1 scholarly publication.
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KEYWORDS
Discrete wavelet transforms

Image compression

Hyperspectral imaging

JPEG2000

Wavelet transforms

Wavelets

Signal to noise ratio

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