Combined particle swarm optimization and modified bilinear model (PSO-MBM) algorithm for nonlinearity detection and spectral unmixing of satellite imageries
2021
Kothandaraman, Niranjani | Kaliaperumal, Vani
The phenomenon of mixed pixels in satellite imagery is very common. Most of the existing unmixing works are based on linear mixing model due to its simplicity. Fan model and generalized bilinear model consider the bilinear interaction between the pixels. But, in many cases, the pixels are supposed to have multiple interactions. In this work ‘Modified Bilinear model’(MBM) is utilized for the nonlinear unmixing process that considers the entire single, bilinear and multiple interactions into account. Even though many nonlinear unmixing models show improved results compared to linear, the nonlinear unmixing of linearly mixed pixels shows even worse results. The Particle Swarm Optimization(PSO) technique is used in many engineering optimization problems but none of them have attempted this technique for Nonlinearity detection. In this work, a new model, Particle Swarm Optmization and Modified Bilinear Model ‘PSO-MBM model’ is proposed to perform the nonlinearity detection and spectral unmixing of satellite images which generalizes the linear, bilinear and nonlinear mixing models. The performance of this detection strategy is evaluated by conducting experiments on both synthetic and real datasets. It is found that the proposed PSO-MBM model has shown better unmixing accuracy comparatively with an average Root Mean Square Error (RMSE) of 0.1411 and average Reconstruction Error (RE) of 0.0678.
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