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Proceedings Paper

Radiometric restoration of airborne imaging spectrometer data
Author(s): Xiaofang Guo; Runsheng Wang; Jicheng Cheng; Qinghua Wang
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Paper Abstract

Quality and interpretability of airborne imaging spectrometer data generally are degraded by two types of radiometric distortions. The first is called edge radiometric distortion or radiometric distortion in scanning direction which is related to the larger viewing field of remote sensor. During the period of data acquisition, some parameters, such as path radiance, atmospheric attenuation and the sun-object-sensor geometry, continuously change with viewing angle, which causes a severe radiometric distortion on airborne imaging spectrometer data in scanning direction. Additionally, non- Lambertin reflection of ground objects and relief of terrain strongly complicate the radiometric distortion. The second is random noise which may be caused by dark current of remote sensor, dust and other environmental factors. It is impossible to do data processing and quantitative analysis if radiometric distortions of data are not corrected properly. This paper discusses and analyses various factors which cause radiometric distortions of airborne imaging spectrometer data. Two new methods based on wavelet transform are developed to correct edge radiometric distortion and remove random noises of airborne imaging spectrometer data. Experimental results presented in the paper illustrate that proposed methods are more practical, effective and efficient for radiometric restoration of airborne imaging spectrometer data.

Paper Details

Date Published: 17 August 1998
PDF: 9 pages
Proc. SPIE 3502, Hyperspectral Remote Sensing and Application, (17 August 1998); doi: 10.1117/12.317783
Show Author Affiliations
Xiaofang Guo, Ctr. for Remote Sensing in Geology (China)
Runsheng Wang, Ctr. for Remote Sensing in Geology (China)
Jicheng Cheng, Peking Univ. (China)
Qinghua Wang, Ctr. for Remote Sensing in Geology (China)

Published in SPIE Proceedings Vol. 3502:
Hyperspectral Remote Sensing and Application
Robert O. Green; Qingxi Tong, Editor(s)

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