A Novel Supervised Linear Spectral Unmixing Model Constrained Pso Approach for Abundance Estimation

The existence of mixed pixels is common in hyperspectral data. Although, the proportion of each spectral signature in a given mixed pixels scenario may be determined through the spectral unmixing operations. In this work, Particle Swarm Optimization (PSO) based approach is proposed in order to estimate the abundances fractions for spectral unmixing. It calculates the position in order to estimate the fractions. In this, the concept of particles per solution is omitted in order to do unmixing. Hence, each pixel of data is our particle, and the solution is its abundance fractions. This approach is less computationally complex as compared to other proposed PSO based approaches for abundance estimation. Herein, supervised linear mixing model and spatially correlated data are the assumptions considered for unmixing operation. The proposed method is tested on simulated data and it has been observed to be performing well.