Research On Remote Sensing Images Mosaicking Method Based On Spatiotemporal Fusion

Due to the limited width of high-resolution satellite,the application of earth observation in large area usually needs to mosaic multiple high-resolution images.These mosaic images captured at different times usually have obvious color differences,so the image should be uniform to avoid obvious stitching and ghosting in the transition area.The current remote sensing image mosaic technology has been able to achieve high visual continuity in eliminating the color difference between images,but it can not maintain the radiation accuracy of the mosaic data,so it can not be applied to quantitative remote sensing application and remote sensing interpretation.This paper takes the advantage of the image color balance technology in the task of remote sensing image mosaic as the breakthrough point,combined with the advantages of remote sensing image spatiotemporal fusion in image reconstruction.The specific research work includes:Firstly,a remote sensing image mosaic framework based on spatiotemporal fusion is proposed to eliminate the color difference between images.In this framework,two low resolution reference images are introduced for each mosaic image,and then all images are reconstructed to a unified time using spatiotemporal fusion method.From the quantitative evaluation indexes in the experiment,the proposed framework achieves good performance in terms of radiation,structure and spectral consistency.In terms of visual effect,the mosaic results of the proposed frame are consistent in overall hue,and the stitches of adjacent images are difficult to detect.Secondly,a new spatiotemporal fusion method based on enhanced depth superresolution network is proposed to solve the problem of poor performance and efficiency of spatiotemporal fusion algorithm in large-scale remote sensing image mosaic.The purpose of enhanced depth super-resolution network is to model the spatial difference between high resolution image and low resolution reference image.In network training,transfer learning is used to solve the problem of insufficient remote sensing image data.Only one training cycle is needed to adapt to the image content in remote sensing mosaic application.The experimental results show that compared with the traditional method,this method runs faster and the average radiation error of reconstructed image is smaller.Finally,in order to prove that the mosaic results can be applied to remote sensing quantitative application,this paper interprets the mosaic results and proposes a pyramid residual network for multispectral image classification.Pyramid residual network can extract more spectral spatial features to obtain higher classification accuracy.The classification results show that the pixel values of mosaic results can be used to distinguish the object types they represent.It can be used to analyze the correlation between them,and then study the distribution law and change process of the regional features.