Wolfgang Middelmann

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Automatically partitioning images into regions ('segmenta-tion') is challenging in terms of quality and performance. We propose a Minimum Spanning Tree-based algorithm with a novel graph-cutting heuristic, the usefulness of which is demonstrated by promising results obtained on standard images. In contrast to data-parallel schemes that divide images into(More)
Training machine learning algorithms for land cover classification is labour intensive. Applying active learning strategies tries to alleviate this, but can lead to unexpected results. We demonstrate what can go wrong when uncertainty sampling with an SVM is applied to real world remote sensing data. Possible causes and solutions are suggested.
In the last few years, unmixing of hyperspectral data has become of major importance. The high spectral resolution results in a loss of spatial resolution. Thus, spectra of edges and small objects are composed of mixtures of their neighboring materials. Due to the fact that supervised unmixing is impossible for extensive data sets, the unsupervised(More)
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