Reducing the size of the data on-ground with no information loss represents a strong challenge for the scientific community, since Earth observation (EO) data volumes have strongly and steadily grown during the last 10 years and the need for more efficient compression methods is growing stronger. High-accuracy processing methods employed for EO data understanding and quantifying may result in effective methods for image compression. We propose to use a robust framework of endmember extraction and nonlinear modeling for the on-ground compression of EO data records, where the distribution of the mixture coefficient is exploited to enhance the compression gain while providing high-accuracy reconstruction. Experimental results over real EO datasets show the actual power of the proposed approach.
"Seizing on sparsity in nonlinear hyperspectral unmixing for enhanced image compression," Journal of Applied Remote Sensing 10(4), 042007 (8 August 2016). https://doi.org/10.1117/1.JRS.10.042007