Remote sensing image fusion based on real time image smoothing and image similarity - Yanfang Hou; Kaixuan Guo; Xueyan Bi (Systems and Soft Computing)
Real-time image smoothing and image similarity of remote sensing image techniques can help people to obtain image information more accurately in the field of remote sensing. To improve the efficiency of information analysis, image fusion techniques are required. In this study, image preprocessing algorithm is used to smooth remote sensing images for the fusion problem between different remote sensing images. Subsequently, a hybrid algorithm model based on non-negative matrix factorization and block term decomposition is used to fuse remote sensing images. The outcomes indicated that the image preprocessing algorithm preprocessed image had better smoothness and performed better in outdoor scenes, with peak signal-to-noise ratio of 28.26 dB and average structural similarity of 0.91. The remote sensing image of urban landscape scenes fused by the hybrid algorithm model, not only had complete spectral information and feature parameters, but also high clarity. The root mean squared error index of the hybrid remote sensing image was 0.0121, the correlation coefficients was 0.9905, the spectral angle mapping was 0.0198, and the running time was 25s. It can be concluded that by preprocessing remote sensing images and then fusing them, not only can information-rich images be obtained quickly, but also the efficiency of image analysis can be greatly improved. The research not only provides a new method for improving the quality and fusion of remote sensing images, but also offers a new technology for denoising remote sensing images.