Publications in Scientific Journals:

C Künzer, M. Bachmann, A. Müller, L. Lieckfeld, W. Wagner:
"Partial unmixing as a tool for single surface class detection and time series analysis";
International Journal of Remote Sensing, 29 (2008), 11; 3233 - 3255.

English abstract:
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 preprocessing
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.

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