A hyperspectral unmixing method based on ADMM and deep generative prior
The presence of mixed pixels restricts the accuracy of hyperspectral image classification and object detec-tion.To improve the accuracy of mixed pixel decomposition and accurately analyze the composition of mixed pixels,this study proposes a hyperspectral unmixing method that combines an optimization method with deep generative pri-ors,thereby achieving an organic combination of data-driven and model-driven approaches.In recent years,deep neural networks have been widely used in hyperspectral unmixing;however,these methods often act as"black bo-xes"lacking physical interpretability.Conversely,traditional mathematically optimized hyperspectral unmixing methods use manually selected priors to introduce intrinsic information and improve the accuracy of results.Howev-er,computing a complex regularizer needs difficult algorithms,and some information cannot be modeled mathemati-cally.In this study,we propose a hyperspectral unmixing method that integrates the alternating direction method of multipliers(ADMMs)with deep generative priors to combine the strengths of both approaches.Specifically,we use ADMM to decompose the data-fitting term and generative priors,and the decoder of a VAE pre-trained by abun-dance calculated using conventional methods is applied as the generator.This study uses simulated and real remote-sensing datasets to evaluate the effectiveness of the proposed method.
万方数据
维普
