Performing standard unmixing of a hyperspectral image, while taking into account all of the potential endmembers (EMs) in a pixel, is known to be prone to error. Instead, determining first the set of EMs that actually reside in each pixel, leads to enhanced unmixing results. This important insight for achieving higher unmixing accuracy can be exploited efficiently by extracting relevant spatial information from a given image. In this work, we present a new method for spatially adaptive spectral unmixing, called the Gaussian based spatially adaptive unmixing (GBSAU) method. GBSAU takes advantage of the spatial arrangement of the image pixels and their spectral relations in order to determine an actual subset of EMs per pixel. It is based on spatial localization of the EMs by fitting, for each EM, the parameters of the series of spatial Gaussians whose sum represents the EM's fraction surface over the image.
|Title of host publication
|2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 10 Nov 2015
|IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: 26 Jul 2015 → 31 Jul 2015
|International Geoscience and Remote Sensing Symposium (IGARSS)
|IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
|26/07/15 → 31/07/15
Bibliographical notePublisher Copyright:
© 2015 IEEE.
- 2D Gaussian fitting
- Spatial endmember localization
- Spectral unmixing