Fast implementation of linear and nonlinear simplex growing algorithm for hyperspectral endmember extraction

Zhao, Liaoying; Fan, Mingyang; Li, Xiaorun; Wang, Lijiao

doi:10.1016/j.ijleo.2015.08.020

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 30 th band grey map of the simulated dataset is shown in Fig.3(b). The endmember spectral was extracted by FNSGA (Zhao et al, 2015) and the fraction maps were generated by Fully Constrained Least Squares (Heinz and Chein-I-Chang, 2002) method on the degraded low-resolution images. Then subpixel localization was performed.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

Zhaoxin

Zhao

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

ABSTRACT:Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

show abstract

Section: Simulation Experimentsmentioning

confidence: 99%