USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT
surfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and inaccurate due to the complexity of urban...
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doaj-d2551020af6a4398aa67709fbf8a6e922020-11-24T22:01:25ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342017-09-01XLII-2-W791992310.5194/isprs-archives-XLII-2-W7-919-2017USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENTT. Wang0T. Wang1H. Zhang2H. Zhang3H. Lin4H. Lin5Institute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong KongShenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, 518057, ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong KongShenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, 518057, ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong KongShenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, 518057, Chinasurfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and inaccurate due to the complexity of urban infrastructures base on the need to further recognize these impervious surface materials in a finer scale. Hyperspectral imageries have been proved to be sensitive to subtle spectral differences thus capable to exquisitely discriminate these similar materials while limited to the low spatial resolution. Coupled nonnegative matrix factorization (CNMF) unmixing method is one of the most physically straightforward and easily complemented hyperspectral pan-sharpening methods that could produce fused data with both high spectral and spatial resolution. This paper aimed to exploit the latent capacity and tentative validation of CNMF on the killer application of mapping urban impervious surfaces in complexed metropolitan environments like Hong Kong. Experiments showed that the fusion of high spectral and spatial resolution image could provide more accurate and comprehensive information on urban impervious surface estimation.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/919/2017/isprs-archives-XLII-2-W7-919-2017.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
T. Wang T. Wang H. Zhang H. Zhang H. Lin H. Lin |
spellingShingle |
T. Wang T. Wang H. Zhang H. Zhang H. Lin H. Lin USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
T. Wang T. Wang H. Zhang H. Zhang H. Lin H. Lin |
author_sort |
T. Wang |
title |
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT |
title_short |
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT |
title_full |
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT |
title_fullStr |
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT |
title_full_unstemmed |
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT |
title_sort |
using coupled nonnegative matrix factorization (cnmf) un-mixing
for high spectral and spatial resolution data fusion to estimate
urban impervious surface and urban ecological environment |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2017-09-01 |
description |
surfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall
environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and
inaccurate due to the complexity of urban infrastructures base on the need to further recognize these impervious surface materials in a
finer scale. Hyperspectral imageries have been proved to be sensitive to subtle spectral differences thus capable to exquisitely
discriminate these similar materials while limited to the low spatial resolution. Coupled nonnegative matrix factorization (CNMF) unmixing
method is one of the most physically straightforward and easily complemented hyperspectral pan-sharpening methods that
could produce fused data with both high spectral and spatial resolution. This paper aimed to exploit the latent capacity and tentative
validation of CNMF on the killer application of mapping urban impervious surfaces in complexed metropolitan environments like
Hong Kong. Experiments showed that the fusion of high spectral and spatial resolution image could provide more accurate and
comprehensive information on urban impervious surface estimation. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/919/2017/isprs-archives-XLII-2-W7-919-2017.pdf |
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