Enhanced Self-Training Superresolution Mapping Technique for Hyperspectral Imagery

Author

Mianji, Fereidoun A. ; Gu, Yanfeng ; Zhang, Ye ; Zhang, Junping

Author_Institution

Dept. of Inf. Eng., Harbin Inst. of Technol., Harbin, China

Volume

8

Issue

4

fYear

2011

fDate

7/1/2011 12:00:00 AM

Firstpage

671

Lastpage

675

Abstract

An efficient superresolution technique through spatial-spectral data fusion for hyperspectral (HS) imagery is proposed in this letter. The spatial and spectral contents of an HS image are extracted using a linear mixture model and a fully constrained least squares unmixing technique. These data are then combined using a spatial correlation model through a learning-based superresolution mapping (SRM) algorithm. The proposed spatial correlation model realistically simulates a mapping model between the low-resolution (LR) HS image and its subsampled version ( LR² HS image) to train the designed SRM algorithm for mapping from the LR to high resolution. The experiments on real HS images validate the accuracy and low complexity of the proposed autonomous technique for key information detection in HS imagery.

Keywords

feature extraction; geophysical image processing; remote sensing; SRM algorithm; autonomous technique; data fusion; hyperspectral imagery; key information detection; least squares unmixing technique; linear mixture model; remote sensing; self-training superresolution mapping technique; spatial correlation model; Hyperspectral imaging; Pixel; Spatial resolution; Training; Data fusion; hyperspectral (HS) imagery; key information detection; linear mixture model (LMM); superresolution mapping (SRM);

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2010.2102334

Filename

5706437