{"title":"Model-based radiometric restoration","authors":"Russel P. Kauffman, P. North, P. M. Fuller","doi":"10.1109/AERO.2010.5446707","DOIUrl":null,"url":null,"abstract":"Radiance restoration via linear filtering is problematic for small targets, and estimating the thermally emitted radiance of sub-pixel targets is very difficult, resulting in large errors. However, with a priori knowledge of the geometry of a scene, a site model can be constructed to aid in radiance estimation. The scene is modeled as a linear combination of known shapes and the radiance of these shapes is adjusted to fit the observed image. This method of estimating the radiances of small to sub-pixel targets can yield significantly lower errors than linear filtering. The performance of the method on synthetic images is discussed as a function of the size, radiance, and background of the target and of the noise in the image. The model-based approach is found to outperform a simple linear filter for very small targets (diameter of a few pixels) with high contrast relative to the image noise.1 2","PeriodicalId":378029,"journal":{"name":"2010 IEEE Aerospace Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Aerospace Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AERO.2010.5446707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Radiance restoration via linear filtering is problematic for small targets, and estimating the thermally emitted radiance of sub-pixel targets is very difficult, resulting in large errors. However, with a priori knowledge of the geometry of a scene, a site model can be constructed to aid in radiance estimation. The scene is modeled as a linear combination of known shapes and the radiance of these shapes is adjusted to fit the observed image. This method of estimating the radiances of small to sub-pixel targets can yield significantly lower errors than linear filtering. The performance of the method on synthetic images is discussed as a function of the size, radiance, and background of the target and of the noise in the image. The model-based approach is found to outperform a simple linear filter for very small targets (diameter of a few pixels) with high contrast relative to the image noise.1 2