• Coding Scheme for the Transmission of Satellite Imagery

      Auli-Llinas, Francesc; Marcellin, Michael W.; Sanchez, Victor; Serra-Sagrista, Joan; Bartrina-Rapesta, Joan; Blanes, Ian; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2016-03)
      The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.
    • Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data

      Amrani, Naoufal; Serra-Sagrista, Joan; Hernandez-Cabronero, Miguel; Marcellin, Michael; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2016-03)
      Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.