Learning Locality-Constrained Sparse Coding for Spectral Enhancement of Multispectral Imagery

Owing to easy acquisition and large coverage from the space, multispectral (MS) imaging has garnered growing interest in various applications of remote sensing. However, the limited spectral information of MS data, to a great extent, leads to difficulties in classifying the materials more accurately...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
Main Authors: Hong, Danfeng, Wu, Xin, Gao, Lianru, Zhang, Bing, Chanussot, Jocelyn
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Coding
Constraints
Hyperspectral (HS)
Hyperspectral imaging
Image coding
Image enhancement
Image reconstruction
Imagery
locality
multispectral (MS)
Optimization
partially
Performance assessment
Remote sensing
remote sensing (RS)
Signal resolution
sparse coding
Spatial resolution
Spectra
spectral enhancement
Spectral resolution
Task analysis
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Owing to easy acquisition and large coverage from the space, multispectral (MS) imaging has garnered growing interest in various applications of remote sensing. However, the limited spectral information of MS data, to a great extent, leads to difficulties in classifying the materials more accurately, particularly for those classes that have very similar visual appearances. To address this issue effectively, we attempt to enhance the spectral resolution of MS imagery, enabling the identification of materials at a more precise level by the means of richer spectral information. More specifically, we propose to learn locality-constrained sparse coding (LCSC) for short, on partially overlapped hyperspectral (HS)-MS pairs (i.e., dictionary). LCSC is capable of capturing neighboring relations well by enforcing the local constraint for each pixel. Such a strategy makes it possible to better reconstruct HS products from MS images and partially overlapped HS images. Reconstruction and unmixing are explored as potential applications to assess the performance of spectral enhancement. Extensive experiments are conducted on two HS-MS data sets in comparison with several state-of-the-art baselines, which demonstrate the effectiveness of the proposed LCSC algorithm in the task of spectral enhancement.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3043402