Paper Info
Title
Super-resolution: an efficient method to improve spatial resolution of hyperspectral images
Abstract
Hyperspectral imaging is a continuously growing area of remote sensing application. The wide spectral range, 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 that the high spectral resolution is usually complementary to the spatial one, which can vary from a few to tens of meters. Many factors, such as imperfect imaging optics, atmospheric scattering, secondary illumination effects and sensor noise cause a degradation of the acquired image quality, making the spatial resolution one of the most expensive and hardest to improve in imaging systems. In this work, a novel method, based on the use of source separation technique and a spatial regularization step by simulated annealing is proposed to improve the spatial resolution of cover classification maps. Experiments have been carried out on both synthetic and real hyperspectral data and show the effectiveness of the proposed method.
Year
DOI
Venue
2010
10.1109/IGARSS.2010.5654208
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
geophysical image processing,image resolution,remote sensing,hyperspectral data,hyperspectral image,hyperspectral imaging,image quality,remote sensing,spatial resolution,spectral resolution,superresolution,surface detection,Hyperspectral data,Simulated annealing,Source separation,Spatial regularization,Super resolution
Computer vision,Full spectral imaging,Computer science,Remote sensing,Image quality,Hyperspectral imaging,Spectral resolution,Remote sensing application,Artificial intelligence,Pixel,Image resolution,Source separation
Conference
ISSN
ISBN
Citations 
2153-6996 E-ISBN : 978-1-4244-9564-1
978-1-4244-9564-1
5
PageRank 
References 
Authors
0.51
6
4
Name
Order
Citations
PageRank
Villa, A.150.51
Jocelyn Chanussot24145272.11
J. A. Benediktsson386083.81
Ulfarsson, M.450.51