Atmospheric Correction Of Quickbird Imagery Using The Ground Surface Spectrum Vector Models

Reflectivity between the recording of imagery and the features of earth surface hasa big difference. Typically, the radiation contribution of surface reflectance accountedfor50%at450nm band and accounted for80%at850nm band in satellite remotesensing images. The features of earth surface blurred and the spectral distortedbecause of the atmospheric scattering and absorption effects. How to build the modelto remove the image atmospheric effects getting a clear image of surface features andobtaining accurate surface reflectance is the problem that atmospheric correction mustbe solve. Also this problem is the most important issue that remote sensingapplications must be addressed. This paper improved the existing spectral vectormodel so that it can correct atmospheric effects of QirckBird imagery. Building theatmospheric correction model according to the mechanism of light propagationwithout DTM and synchronization of atmospheric measurement data, solving modelwhich can adapt to the changes of the terrain factor, the BRDF factor and mixed-likeelement in each of the surface features, correct the atmospheric expecting thecorrected image have same effect between mountains and plains, between mixed pixeland pure pixel.This paper improved the correction method of atmospheric cross radiation effectbetween the neighboring pixel and the method of atmospheric images stripped inspectral vector model. At the first, classify the study area corroding to the spectralvector of the surface features. And then introduce the modified least square method tomodify the model to make it applicable in the Quickbird imagery. At the last theresults of experimental show this methods is effective.The full text is divided into five chapters. Chapter1introduces the content, thestatus, the objectives and the significance of research. The second chapter describesthe effects of atmosphere briefly. How to improve the ground surface spectrum vectormodels is described in detail. The third chapter includes the introduction of the IDLlanguage and the main code having simple instructions. The fourth chapter, taking thecontent of the last two paragraphs, applies the spectrum vector model to atmosphericcorrection of the Quickbird image, including the introduction of data, the steps of theexperiment, the results and evaluation of the experiment. The images corrected by ground surface spectrum vector model are more limpid, and they are better in vision,too. The relative error is about15%in Quickbird imagery band1, and is about4%inQuickbird imagery band2. The last chapter is the conclusions obtained in thisstudy.The main progress, the problems in this article and the outlook for the nextwork have carried out.