A low-complexity destriping method for lossless compression of remote-sensing data

Remote sensing are widely used in applications including geoexploration, topographic mapping and weather forecasting, producing vast amounts of multi and hyper-spectral image data that need to be compressed [1]. The data acquisition process often leads to artifacts in the form of stripes with unpredictable positions and amplitudes [2]. The stripes deteriorate the smoonthless of the original image, causing challenges for high-ratio lossless compression. This motivates us to propose a split-and-compress framework. Rather than direct compression, we split (decompose) the image into a smooth part and a sparse remainder (capturing the stripes and artifacts alike) and compress the two parts separately. The decomposition is achieved using a fast, robust statistics based method with linear computational complexity on the number of pixels.