Super-Resolution Reconstruction from the Incompletely Observed Remote Sensing Images

  • MISAIZU Hiroyuki
    R & D Center, Tokyo Electric Power Company Graduate School of Engineering, Gunma University
  • INAMURA Minoru
    Graduate School of Engineering, Gunma University

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  • 不完全なリモートセンシング観測画像からの超解像再構成
  • フカンゼン ナ リモートセンシング カンソク ガゾウ カラ ノ チョウカイゾウ サイコウセイ

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Abstract

In satellite remote sensing, higher spatial resolution than the particular resolution of a sensor may be achieved by observing a same area repeatedly at an interval decided by the recurrent interval days. The methods have been researched to improve the spatial resolution by fusion of multiple low-resolution images and by the multiple observations of subpixel-shifted low-resolution images. These methods are known as the super-resolution (SR) reconstruction. Various methods of the SR reconstruction have been suggested till now. When resolution is raised N times by the SR reconstruction, peculiar subpixel-shifted images of about N×N numbers or even more are required. Uncontrollable misregistration by slight orbital translation may often be present in the observed images. And, it can be actually obtained limited pieces of observed images under a same weather condition in a same season. In this paper, a new SR reconstruction method is proposed by using a number of incomplete low-resolution images with random positional shifts. This proposed method enables sufficient SR performance by giving the preliminally process such as re-arrangement of elements of low-resolution images on the continuous space and supplementation with insufficient low-resolution images by the 2-dimensional interpolation. The simulation using sample images and the experiment using actual images taken in the laboratory shows effectiveness.

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