ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 161–165, 2018
https://doi.org/10.5194/isprs-annals-IV-3-161-2018
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 161–165, 2018
https://doi.org/10.5194/isprs-annals-IV-3-161-2018

  23 Apr 2018

23 Apr 2018

SUBPIXEL MAPPING OF HYPERSPECTRAL IMAGE BASED ON LINEAR SUBPIXEL FEATURE DETECTION AND OBJECT OPTIMIZATION

Zhaoxin Liu1, Liaoying Zhao1, Xiaorun Li2, and Shuhan Chen2 Zhaoxin Liu et al.
  • 1Institute of Computer Application Technology, Hangzhou Dianzi University, China
  • 2College of Electrical Engineering, Zhejiang University, China

Keywords: Hyperspectral Imagery, Linear Subpixel Feature Detection, Subpixel Mapping, Space Correlation,Template Matching

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.