View in EDS

TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm.

Saved in:
Bibliographic Details
Title: TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm.
Authors: Teggi, S., Cecchi, R., Serafini, F.
Source: International Journal of Remote Sensing. 3/20/2003, Vol. 24 Issue 6, p1287. 15p.
Subjects: Remote sensing, Multisensor data fusion
Abstract: Image fusion represents an important tool for remote sensing data elaborations. This technique is used for many purposes. Very often it is used to produce improved spatial resolution. The most common situation is represented by a pair of images: the first acquired by a multispectral sensor with a pixel size greater than the pixel size of the second image given by a panchromatic sensor (PAN). Starting from these images fusion produces a new multispectral image with a spatial resolution equal, or close, to that of the PAN. Very often fusion introduces important distortions on the pixel spectra. This fact could compromise the extraction of information from the image, especially when using an automatic algorithm based on spectral signature such as in the case of image classification. In this work we present the analysis of two fusion methods based on multiresolution decomposition obtained using the 'a tröus' algorithm and applied to a pair of images acquired by Thematic Mapper (TM) and Indian Remote Sensing (IRS)-1C-PAN sensors. The methods studied are also compared with two classical fusion methods, the intensity, hue and saturation (IHS) and standardized principal components (SPC). Fused results are studied and compared using various tests including supervised classification. Most of the tests used have been extracted from literature regarding the assessment of spatial and spectral quality of fused images. This study shows that the methods based on multiresolution decomposition outperform the classical fusion methods considered with respect to spectral content preservation. Moreover, it is shown that some of the quality tests are more significant than others. The discussion of this last aspect furnishes important indications for data quality assessment methods. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Image fusion represents an important tool for remote sensing data elaborations. This technique is used for many purposes. Very often it is used to produce improved spatial resolution. The most common situation is represented by a pair of images: the first acquired by a multispectral sensor with a pixel size greater than the pixel size of the second image given by a panchromatic sensor (PAN). Starting from these images fusion produces a new multispectral image with a spatial resolution equal, or close, to that of the PAN. Very often fusion introduces important distortions on the pixel spectra. This fact could compromise the extraction of information from the image, especially when using an automatic algorithm based on spectral signature such as in the case of image classification. In this work we present the analysis of two fusion methods based on multiresolution decomposition obtained using the 'a tröus' algorithm and applied to a pair of images acquired by Thematic Mapper (TM) and Indian Remote Sensing (IRS)-1C-PAN sensors. The methods studied are also compared with two classical fusion methods, the intensity, hue and saturation (IHS) and standardized principal components (SPC). Fused results are studied and compared using various tests including supervised classification. Most of the tests used have been extracted from literature regarding the assessment of spatial and spectral quality of fused images. This study shows that the methods based on multiresolution decomposition outperform the classical fusion methods considered with respect to spectral content preservation. Moreover, it is shown that some of the quality tests are more significant than others. The discussion of this last aspect furnishes important indications for data quality assessment methods. [ABSTRACT FROM AUTHOR]
ISSN:01431161
DOI:10.1080/01431160210144561