The problem of classification of hyperspectral images containing mixed pixels is addressed. Hyperspectral imaging is a continuously growing area of remote sensing applications. The wide spectral range of such imagery, providing a very high spectral resolution, allows to detect and classify surfaces and chemical elements of the observed image. The main problem of hyperspectral data is the (relatively) low spatial resolution, which can vary from a few to tens of meters. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. For classification, the major problem caused by low spatial resolution are the mixed pixels, i.e., parts of the image where more than one land cover map lie in the same pixel. In this paper, we propose a method to address the problem of mixed pixels and to obtain a finer spatial resolution of the land cover classification maps. The method exploits the advantages of both soft classification techniques and spectral unmixing algorithms, in order to determine the fractional abundances of the classes at a sub-pixel scale. Spatial regularization by simulated annealing is finally performed to spatially locate the obtained classes. Experiments carried out on synthetic real data sets show excellent results both from a qualitative and quantitative point of view.
|Number of pages||13|
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|Publication status||Published - Jun 2011|
Bibliographical noteFunding Information:
Manuscript received May 04, 2010; revised October 03, 2010; accepted November 24, 2010. Date of publication December 06, 2010; date of current version May 18, 2011. This work was supported by the European Community’s Marie Curie Research Training Networks Program under contract MRTN-CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Lorenzo Bruzzone.
- Hyperspectral data
- simulated annealing
- source separation
- spatial regularization
- spatial resolution improvement