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A Probability Model-based Method for Land Cover Change Detection Using Multi-Spectral Remotely Sensed Images Ein wahrscheinlichkeits- und modellbasiertes Verfahren zur Veränderungsanalyse der Landnutzung durch Nutzung multi-spektraler Fernerkundungsdaten

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Change detection is one of the main research areas in remotely sensed image processing. Image differencing methods have been widely used to quantify changed pixels by labeling such pixels with differencing images. There is room, however, to further develop the approach by enhancing the change detection reliability method by reducing the index sensitivity to seasonal variations. Using the information provided by image differencing results, a probability model-based change detection method is proposed in this study. A Chi-square distribution model is built using multiple index images based on the assumption that the pixels in the differencing image follow a normal distribution. By means of Chi-square distribution percentiles, different probability contours can be found to differentiate the changed pixels from all pixels in the feature space. The pixels located outside the probability contour will then, be identified as the changed pixels with a certain probability level. Tasseled Cap transformation components can be utilized to construct the Chi-square distribution, thus obtaining a higher accuracy of change detection. Due to the availability of multiple index images such as NDVI and Tasseled Cap transformation components, ETM+ images of Hong Kong on Aug. 20, 1999 and Sep. 17, 2002 were used as experimental data to test the performance of the proposed method. The experiments showed that the combination of NDVI and Brightness indices produced the highest overall accuracy and Kappa coefficient values.

Die Veränderungs-analyse (Change Detection) ist ein sehr wichtiges Forschungsfeld in der fernerkundlichen Bildverarbeitung. Zwar sind Change Detection Methoden in der Vergangenheit schon häufig benutzt worden, um Veränderungen kenntlich zu machen, allerdings gibt es immer noch Potential zur Verbesserung der Methoden insbesondere zu deren Zuverlässigkeit gegenüber saisonalen Schwankungen der Landnutzung. In diesem Artikel wird ein wahrscheinlichkeits- und modellbasiertes Verfahren zur Veränderungsanalyse vorgestellt. Hierzu wird ein Chi-Quadrat-Verteilungsmodell durch die Nutzung multipler Indexbilder erstellt mit der grundsätzlichen Annahme, dass die Pixel in den zu untersuchenden Bildern der Normalverteilung folgen. Durch die Nutzung des Perzentilwertes der Chi-Quadrat-Verteilung können unterschiedliche Wahrscheinlichkeitskonturen gefunden werden, um die veränderten Pixel von allen anderen Pixeln mit einer bestimmten Wahrscheinlichkeit im Merkmalsraum zu unterscheiden. Die Tasseled Cap Transformation sollte verwendet werden, um die Chi-Quadrat-Verteilung zu konstruieren, so dass man eine höhere Genauigkeit zur Erkennung von Änderungen erhält. Die Leistungsfähigkeit der vorgestellten Methode wurde durch eine Vielzahl von verschiedenen Index-Bildern, wie z. B. den NDVI und die Tasseled Cap Transformation, und ETM-Bildern von Hongkong vom 20. August 1999 und 17. September 2002 getestet. Die Analysen zeigen, dass die Kombination von NDVI- und Brightness-Indizes die höchste Gesamtgenauigkeit und den besten Kappa Koeffizient ergeben.
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Document Type: Research Article

Publication date: 2011-08-01

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  • Photogrammetrie - Fernerkundung - Geoinformation (PFG) is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the intricately connected field of geoinformation processing.

    Papers published in PFG highlight new developments and applications of these technologies in practice. The journal hence addresses both researchers and student of these disciplines at academic institutions and universities and the downstream users in both the private sector and public administration.

    PFG places special editorial emphasis on the communication of new methodologies in data acquisition, new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general.

    PFG is the official journal of the German Society of Photogrammetry and Remote Sensing.
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