Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration

Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new fusion method is proposed to improve the fusion per...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 10; no. 4; p. 592
Main Authors Teng, Yidan, Zhang, Ye, Ti, Chunli, Zhang, Junping
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2018
Subjects
Adaptive algorithms
Adaptive filters
Algorithms
Calibration
Dictionaries
Filtration
hyperspectral
Image enhancement
Image processing
Image resolution
local adaptive sparsification
Methods
Morphology
optimal matching adaptive morphology filtering
Optimization
Pixels
Remote sensing
Sparsity
Spatial discrimination
Spatial resolution
Spectra
Spectral composition
Spectrum analysis
subpixel calibration
unmixing based fusion
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Summary:Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new fusion method is proposed to improve the fusion performance by taking further advantage of the distribution characteristics of ground objects. First, we put forward a local adaptive sparse unmixing based fusion (LASUF) algorithm, in which the sparsity of the abundance matrices is appended as the constraint to the optimization fusion, considering the limited categories of ground objects in a specific range and the local correlation of their distribution. Then, to correct the possible original subpixel misregistrations or those introduced by the fusion procedures, a subpixel calibration method based on optimal matching adaptive morphology filtering (OM-AMF) is designed. Experiments on various datasets captured by different sensors demonstrate that the proposed fusion algorithm surpasses other typical fusion techniques in both spatial and spectral domains. The proposed method effectively preserves the spectral composition features of the isolated ground objects within a small area. In addition, the OM-AMF postprocessing is able to spatially correct the fusion results at a subpixel level and preserve the spectral features simultaneously.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs10040592