Research On Hyperspectral Remote Sensing Image Fusion And Edge Extraction

Hyperspectral remote sensing images have a large number of spectral bands,which contribute to the detailed classification and identification of ground objects.However,with the bands increasing,the data redundancy is raised correspondingly,making the image fusion computation and the process complicated,and the loss of weak edges are caused by fusion image edge extraction.Therefore,this dissertation mainly focuses on the fusion of hyperspectral remote sensing images and fusion image edge extraction,and makes the following research:(1)Aiming at the spectral distortion and lack of detail information of the original image in traditional hyperspectral remote sensing image fusion,a band background clarity based wavelet weighted average method is proposed for hyperspectral image fusion in this dissertation.Firstly,using J-M distance and the principle of the best index selection,the optimal band and the preferred band combination are extracted from hyperspectral remote sensing images to reduce the band data redundancy and improve the information complementarity.Secondly,the EM algorithm for singleband remote sensing image background clarity is adopted for the selected wavebands to improve the clear quality of the spectral band image.Finally,the pixel level of wavelet weighted average is used to optimize the enhanced data of the band remote sensing image,which makes the quality of the fused image better.The experimental results show that our method improves the standard deviation,information content and clarity index of the fused image,which solves better the problem of data redundancy in image fusion and also achieves better fusion quality of hyperspectral remote sensing image.Meanwhile,it also improves the quality of fused images greatly.(2)Because the traditional image edge extraction method is incomplete and unclear for image edge extraction,this dissertation proposes a bilateral filtering algorithm based on the gradient magnitude of the modified Canny operator.The traditional Canny algorithm is improved by using the 33? neighborhood instead of the 22? neighborhood,and computes the gradient magnitude by calculating the first-order partial derivative of the pixel 8 neighborhood first.Then the fusion image of improved Canny operator gradient amplitude is carried out bilateral filtering to protect and enhance the edge information of remote sensing fusion image.The experimental results show that our algorithm has the highest quality coefficient,the shortest running time,the higher signal-to-noise ratio and positioning accuracy,and the longer average distance at the zero-crossing point for the fusion image edge extraction,which can better solve the problem of the edge missing of the fused image.Meanwhile,it also shows that the edge information of fusion image extracted by our algorithm is richer and more continuous.