Remote sensing image building edge detection technology based on NSCT and tensor decomposition
The edges of buildings obtained in remote sensing images by common edge detection and semantic segmentation techniques are not fine enough and can not reflect the details of the building roof.To solve these problems,an edge detection technique based on Non Subsampled Contourlet Transform(NSCT)combined with tensor decomposition is proposed to obtain the edge features of building detaileds including roof.Firstly,NSCT is used to decompose the image to obtain the sub-band frequency information of different scales and angles.Secondly,these subband coefficients are encoded to obtain second-order symmetric tensors of corresponding positions,and then the tensors of different scales and angles at the same position are weighted and summed to complete the feature fusion.Finally,the edge features of the image are obtained by tensor decomposition according to spectral theory.The experimental results show that compared with other five detection algorithms such as BDCN(Bi-Directional Cascade Network),the index of peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)of the proposed method is superior to other methods.Compared with BDCN method based on deep learning,PSNR and SSIM increase by 1.20 and 0.03 respective-ly,and the detection results can more accurately and meticulously reflect the boundary information of buildings and the edge information of roofs,which can provide better support for the classifica-tion and style analysis of buildings.
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