MOLECULE-BASED SPARSE REPRESENTATION STRATEGY AS A REPLACEMENT SOLUTION OF ORTHOGONAL MATCHING PURSUIT TECHNIQUE FOR DECOMPOSITION OF TIME SERIES OF VEGETATION INDICES

Abstract

This paper proposes a new strategy based on introducing the molecules as a replacement alternative to the well-known OMP method for a sparse representation of the time series of vegetation indices. This method, by preventing the occurrence of leakage problems, has led to improving the accuracy of the soft classification of agricultural products. To do so, a library of molecules (combination of atoms) is firstly generated by the representative ground truth data as the probable cultivation patterns, and then each time series of vegetation index is decomposed based on molecules. The efficiency of each molecule is measured through three different criteria 1- Maximizing the accuracy of reconstructing the input signal, 2- Minimizing the number of contributed atoms in the molecule, and 3- Minimizing the estimated negative abundances. Then, considering the uncertainties of representative atoms, an iterative imposition of constraints has been used to balance the estimated abundances. The proposed method has improved by 25.21% compared to the common OMP method in the soft classification of temporal signals of vegetation indices.