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In this paper we present the results of time series analysis for a coal mining region based on partial unmixing. We test the method also known as mixture tuned matched filtering on an eight image Landsat 5 TM and Landsat 7 ETM+ time series covering the period from 1987 to 2003. Common change detection methods often include the comparison of two interactively generated classification results, such as derived from Maximum Likelihood classification. These approaches often yield highly accurate results. However, disadvantages include a strong analyst bias and hardly repeatable results. For a quantitative monitoring of a single surface class' development over time they are often not recommendable. Our goal was to test an unbiased quantitative way to assess the development of coal surfaces, such as outcropping coal seams, coal storage piles, coal waste piles, and coal washery discard, within multiple date satellite imagery. Partial unmixing approaches were developed to detect one or few target materials surrounded by—or mixed with—an unknown background material. The main advantage is that only the spectral characteristics of the material of interest must be known, and the desired material can furthermore occur with subpixel coverage. Crisp pixel classificators like maximum likelihood on the other hand require knowledge of all classes. They can only map materials which dominate a pixel. Linear unmixing procedures such as partial unmixing require a thorough radiometric pre-processing of data. Furthermore, the accuracy and representativity of selected input spectra must be granted. In this paper we demonstrate that partial unmixing is a powerful method to detect and extract single landcover classes of interest relatively fast and unbiased. The subpixel fraction percentages should be interpreted in a relative way only. We furthermore show that partial unmixing represents a standardized method for time series analyses and allows for a quantitative assessment of the temporal development of an area. Challenges lie in the validation of partial unmixing results, which we realized through thresholding of unmixing results and accuracy assessment with ground truth polygons mapped in situ. Furthermore, we performed an indirect comparison with results of a multi-endmember unmixing.
Institute of Photogrammetry and Remote Sensing (IPF), Vienna University of Technology, A-1040 Vienna, Austria 2:
Martin Bachmann, Andreas Mueller, Lena Lieckfeld, German Remote Sensing Data Centre, DFD, DLR, D-82234 Wessling, Germany