Kriging-Based Technique for Remote Sensing Image Restoration
The objective of this paper is to examine the effect of restoration of remotely sensed images by three geostatistical approaches. Ordinary Kriging (OK), Universal Kriging (UT), and Indicator Kriging (IK).Using the undersampled data of NDVT from NOAA/AVHRR image, we obtained OK and UK estimates and E-type estimates of IK, OK and UK variances and conditional variance of IK. After comparison, we found that the images of OK and UK estimates can both successfully restore the overall trend of the original image, but the image of E-type estimates of IK is not good enough. We also found that the images of OK and UK variances only reflect the sampling configuration because OK and UK variances are independent of the data values locally, however, owing to the fact that conditional variances of IK are conditional on the data values, they show the errors of estimates perfectly, and the magnitudes of conditional variances are consistent with the uncertainty of remotely sensed data.
Xiaowei Jiang Li Wan Qiang Du Bill Hu
School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China China Institute of Water Resources and Hydropower Rearch, Beijing 100044, China Department of Geological Sciences, Florida State University, Tallahassee, Florida 32306, USA
国际会议
The 12th Conference of the International Association for Mathematical Geology(第12届国际数学地质大会)
北京
英文
429-433
2007-08-26（万方平台首次上网日期，不代表论文的发表时间）