AI SearchData fusion of multi-sensor satellite images
Call Number: AIT Thesis no.CS-93-31 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. CS-93-31Publication details: Bangkok : Asian Institute of Technology, 1993Description: 77 leaves + 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M.Sc.) - Asian Institute of Technology, 1993 Summary: Some techniques for data fusion of different sensor imagery are described. Implementations have been done on Landsat Thematic Mapper with JERS-1 SAR images, and Landsat MSS with JERS-1 SAR images. All data are with different spatial resolution. First, the digital image from one sensor was geometrically registered to the other, re sampling and interpolation algorithms were developed. Next, four color composite methods of band replacement, band ratioing composite, Principal Components Analysis and IHS transformation were developed. The information contents-partial and spectral - were mixed to generate a single data set that contained the best of both sets. In the study, comparative studies among the various compositions were made to evaluate the image quality and interpretability. An effective algorithm of Principal Components Analysis was developed to implement the PCA color composite.
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A thesis submitted in partial fulfillment of the requirement for the degree of Master of Science
Thesis (M.Sc.) - Asian Institute of Technology, 1993
Some techniques for data fusion of different sensor imagery are described. Implementations have been done on Landsat Thematic Mapper with JERS-1 SAR images, and Landsat MSS with JERS-1 SAR images. All data are with different spatial resolution. First, the digital image from one sensor was geometrically registered to the other, re sampling and interpolation algorithms were developed. Next, four color composite methods of band replacement, band ratioing composite, Principal Components Analysis and IHS transformation were developed. The information contents-partial and spectral - were mixed to generate a single data set that contained the best of both sets. In the study, comparative studies among the various compositions were made to evaluate the image quality and interpretability. An effective algorithm of Principal Components Analysis was developed to implement the PCA color composite.
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