![]() We also compare our result with the well known support vector machine (SVM) classifier. A manually generated classification served as reference. In order to evaluate the suggested approach we applied it to a test area in Oberpfaffenhofen, Germany. This code is then matched to that of a training data set for classification. Together with the spectral information these parameters and the corresponding height values per region from the nDSM are converted into a binary code. Five parameters are defined to describe the size and shape of the region, namely area, asymmetry, rectangular fit, ratio of length to width, and compactness. The mean spectrum per region is considered representative for the region. After georeferencing the different data sets and deriving a normalized digital surface model (nDSM), connected regions are extracted from the hyperspectral data by applying an edge-based segmentation algorithm. ![]() ![]() In this paper, an approach is proposed to integrate hyperspectral image data, object and height information into a new region-based binary encoding algorithm for automatically deriving land cover information. ![]()
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December 2022
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