Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2609-2614, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2609-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2609-2614, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2609-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 May 2018

02 May 2018

SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES

R. Zhuo1, L. Xu1, J. Peng1, and Y. Chen2 R. Zhuo et al.
  • 1Dept. of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, China
  • 2Land Consolidation and Rehabilitation Center, Ministry of Land and Resource, Beijing, China

Keywords: Time series Landsat 8 image, Endmember Estimation, K-P-Means, Purified Pixels, Spectral Unmixing

Abstract. Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the “purified” pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of “purified” pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed “joint unmixing” approach provides more accurate endmember and abundance estimation results compared with “separate unmixing” approach.