Hyperspectral imagery super-resolution by sparse representation and spectral regularization
<p>Abstract</p> <p>For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect...
Main Authors: | Zhao Yongqiang, Yang Jinxiang, Zhang Qingyong, Song Lin, Cheng Yongmei, Pan Quan |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2011-01-01
|
Series: | EURASIP Journal on Advances in Signal Processing |
Subjects: | |
Online Access: | http://asp.eurasipjournals.com/content/2011/1/87 |
Similar Items
-
Hyperspectral Imagery Super-Resolution by Compressive Sensing Inspired Dictionary Learning and Spatial-Spectral Regularization
by: Wei Huang, et al.
Published: (2015-01-01) -
Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution
by: Xuesong Li, et al.
Published: (2021-01-01) -
Fast Image Super-resolution with Sparse Coding
by: Yuan Zhi-chao, et al.
Published: (2016-01-01) -
Spatial-Spectral Graph Regularized Kernel Sparse Representation for Hyperspectral Image Classification
by: Jianjun Liu, et al.
Published: (2017-08-01) -
A Super-resolution Reconstruction Method of Remotely Sensed Image Based on Sparse Representation
by: Hui Zhou, et al.
Published: (2013-12-01)