Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 551-555, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-551-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 551-555, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-551-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

THE STUDY OF SPECTRUM RECONSTRUCTION BASED ON FUZZY SET FULL CONSTRAINT AND MULTIENDMEMBER DECOMPOSITION

Y. Sun1, Y. Lin2, X. Hu2, S. Zhao2, S. Liu3, Q. Tong4, D. Helder5, and L. Yan2 Y. Sun et al.
  • 1Chinese Academy of Surveying & Mapping, Beijing 100039, China
  • 2Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
  • 3Guizhou Normal University, Guiyang, Guizhou 550001, China
  • 4Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
  • 5South Dakota State University, South Dakota 57007, USA

Keywords: Multi-spectral; Hyper-spectral; Reconstruction; Fuzzy sets; Multi-endmember; Spectral library

Abstract. Hyperspectral imaging system can obtain spectral and spatial information simultaneously with bandwidth to the level of 10 nm or even less. Therefore, hyperspectral remote sensing has the ability to detect some kinds of objects which can not be detected in wide-band remote sensing, making it becoming one of the hottest spots in remote sensing. In this study, under conditions with a fuzzy set of full constraints, Normalized Multi-Endmember Decomposition Method (NMEDM) for vegetation, water, and soil was proposed to reconstruct hyperspectral data using a large number of high-quality multispectral data and auxiliary spectral library data. This study considered spatial and temporal variation and decreased the calculation time required to reconstruct the hyper-spectral data. The results of spectral reconstruction based on NMEDM showed that the reconstructed data has good qualities and certain applications, which makes it possible to carry out spectral features identification. This method also extends the application of depth and breadth of remote sensing data, helping to explore the law between multispectral and hyperspectral data.