Research On Remote Sensing Image Fusion Model And Optimization Method

Remote sensing image fusion aims at combining multi-source images containing complementary information to obtain one image for richer information and better quality.The fused result can improve the accuracy of target classification and object recognition,which is of practical significance and application value.So far,researchers have designed many fusion models from different angles and achieved good results,but problems still exist,such as focusing on models rather than optimization,unclear scopes of application and so on.For solving these problems,three remote sensing image fusion models with four optimization methods are presented in this paper to design targeted algorithms for different practical application problems.A novel variational pan-sharpening model(PADMM)is proposed,which creates a variational framework with the integration of three hypothesis—spectral consistency,spatial information preservation and the low rank property of images,then optimized by the ADMM algorithm.Experiments showed that the proposed method improves the quality of fused images with high efficiency,which is suitable for data support of post-disaster emergency rescue service.However,the PADMM method has some shortcomings,such as the manual setting of parameters.To avoid such issue,the IHS evolution method(EIHS)and panchromatic adaptive method(PAIHS)are proposed by combining the IHS spatial transformation with the guided filtering and particle swarm optimization.EIHS regards the statistical characteristics of the problem as heuristic information to help the design of evolution operators by introducing simple chromosomes and reasonable fitness functions.The combined differential evolution algorithm(CoDE)is adopted to optimize the function.Experiments demonstrated that EIHS could achieve satisfactory fusion results.Based on EIHS,PAIHS method utilizes the guided filtering to preserve and enhance the edge of the MS image,and then applies particle swarm optimization(PSO)algorithm with higher computational efficiency to optimize the proposed model.Experimental results illustrated that the proposed method outperforms EIHS method in terms of the fusion quality.The above three methods(PADMM,EIHS and PAIHS)have achieved good fusion quality,but they obtain fused images only by optimizing a single model,yet without considering the advantages of combining multiple models.Therefore,this paper finally proposed a pan-sharpening method(MIHS)based on multi-objective optimization.Firstly,two fusion models with complementary advantages are proposed,and then the improved NSGAII algorithm is used to optimize them.A large number of experiments demonstrated the superior quality of pan-sharpening images obtained by the proposed method.Among EIHS,PAIHS and MIHS methods which are based on intelligent optimization,MIHS has the highest time complexity.Thus it is not suitable for providing data services for such timely tasks in a timely manner as post-disaster emergency rescue,but it can provide higher quality and more reliable data services for non-timely work like disaster assessment,disaster analysis and post-disaster reconstruction,etc.The successful implementation of this study framework will provide new models and technical support for remote sensing image fusion,as well as new theories and data services for post-disaster emergency rescue,disaster analysis and post-disaster reconstruction,and in the meantime it will lay the foundation for the application of remote sensing image fusion technology in geological disasters.