Nonlinear endmember extraction in earth observations and astroinformatics data interpretation and compression

As remotely sensed Big Data applications in astrophysics research have been flourishing in the last decade, the need for a new class of techniques and methods for efficient storage, compression, retrieval and investigation of astronomical datasets has become urgent. In this paper, a novel strategy for lossless compression of large datasets composed by remote sensing records is introduced. Specifically, the new approach aims at describing each sample of the given dataset as a point living within a convex hull in a multidimensional space. Thus, the proposed framework aims at characterizing every sample as a nonlinear combination of the extremal points of the aforesaid multidimensional simplex. Therefore, efficient compression can be achieved by describing those samples by the parameters that drive the nonlinear mixture only. Experimental results show how the proposed architecture can effectively deliver great compression performance for both Earth observations and planetary records.