Paper Info
Title
A Hierarchical Sparsity Unmixing Method to Address Endmember Variability in Hyperspectral Image.
Abstract
With a low spectral resolution hyperspectral sensor, the signal recorded from a given pixel against the complex background is a mixture of spectral contents. To improve the accuracy of classification and subpixel object detection, hyperspectral unmixing (HU) is under research in the field of remote sensing. Two factors affect the accuracy of unmixing results including the search of global rather than local optimum in the HU procedure and the spectral variability. With that in mind, this paper proposes a hierarchical weighted sparsity unmixing (HWSU) method to improve the separation of similar interclass endmembers. The hierarchical strategy with abundance sparsity representation in each layer aims to obtain the global optimal solution. In addition, considering the significance of different bands, a weighted matrix of spectra is used to decrease the variability of intra-class endmembers. Both simulations and experiments with real hyperspectral data show that the proposed method can correctly obtain distinct signatures, accurate abundance estimation, and outperforms previous methods. Additionally, the test data shows that the mean spectral angle distance is less than 0.12 and the root mean square error is superior to 0.01.
Year
DOI
Venue
2018
10.3390/rs10050738
REMOTE SENSING
Keywords
Field
DocType
hyperspectral unmixing,hierarchical sparsity constraint,weighted matrix,endmember variability
Computer vision,Object detection,Endmember,Pattern recognition,Local optimum,Mean squared error,Hyperspectral imaging,Pixel,Artificial intelligence,Abundance estimation,Subpixel rendering,Geology
Journal
Volume
Issue
ISSN
10
5
2072-4292
Citations 
PageRank 
References 
4
0.40
16
Authors
3
Name
Order
Citations
PageRank
Jinlin Zou191.55
Jinhui Lan2216.55
Yang Shao3618.17