NDLI: Non-linear spectral mixture analysis of Landsat imagery by means of neural networks

Content Provider	IEEE Xplore Digital Library
Author	Mitraka, Z. Del Frate, F.
Copyright Year	2015
Description	Author affiliation: Dept. of Civil Eng. & Comput. Sci., Univ. of Tor Vergata, Rome, Italy (Mitraka, Z.; Del Frate, F.)
Abstract	Urban surfaces are highly inhomogeneous because of the high spatial and spectral diversity of man-made structures. Spectral unmixing techniques although developed to be used with hyperspectral data, are useful for assessing sub-pixel information on multispectral data as well. The large spectral variability imposes the use of multiple endmember spectral mixture analysis techniques, in which many possible mixture models are considered to produce the best fit. The use of many endmembers and mixture models result in prohibitive computational time. In this study, an artificial neural network is used to inverse the pixel spectral mixture in Landsat imagery. Endmember spectra, collected from the image were used to train the network and capture the spectral variability of man-made structures.
Starting Page	1765
Ending Page	1768
File Size	966302
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Satellites Earth Vegetation mapping Spatial resolution Green products Landsat Urban spectral unmixing neural networks
Content Type	Text
Resource Type	Article

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