Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 919-923, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-919-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 919-923, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-919-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

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

T. Wang1,2, H. Zhang1,2, and H. Lin1,2 T. Wang et al.
  • 1Institute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong Kong
  • 2Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, 518057, China

Keywords: Data fusion; Impervious surface; Coupled Nonnegative Matrix Factorization

Abstract. 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.