High-resolution landform classification using fuzzy k-means
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Principles of semantic modeling of landform structures
Computers & Geosciences - Special issue on GeoComp 99- GeoComputation and the Geosciences
SRTM-based morphotectonic analysis of the Poços de Caldas Alkaline Massif, southeastern Brazil
Computers & Geosciences
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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In this paper, we demonstrate artificial neural networks-self-organizing map (SOM)-as a semi-automatic method for extraction and analysis of landscape elements in the man and biosphere reserve ''Eastern Carpathians''. The Shuttle Radar Topography Mission (SRTM) collected data to produce generally available digital elevation models (DEM). Together with Landsat Thematic Mapper data, this provides a unique, consistent and nearly worldwide data set. To integrate the DEM with Landsat data, it was re-projected from geographic coordinates to UTM with 28.5m spatial resolution using cubic convolution interpolation. To provide quantitative morphometric parameters, first-order (slope) and second-order derivatives of the DEM-minimum curvature, maximum curvature and cross-sectional curvature-were calculated by fitting a bivariate quadratic surface with a window size of 9x9 pixels. These surface curvatures are strongly related to landform features and geomorphological processes. Four morphometric parameters and seven Landsat-enhanced thematic mapper (ETM+) bands were used as input for the SOM algorithm. Once the network weights have been randomly initialized, different learning parameter sets, e.g. initial radius, final radius and number of iterations, were investigated. An optimal SOM with 20 classes using 1000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. The effect of randomization of initial weights for optimal SOM was also studied. Feature space analysis, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of landform, based on morphometric analysis, and land use, based on spectral properties. Results were displayed as thematic map of landscape elements according to form, cover and slope. Spectral and morphometric signature analysis with corresponding zoom samples superimposed by contour lines were compared in detail to clarify the role of morphometric parameters to separate landscape elements. The results revealed the efficiency of SOM to integrate SRTM and Landsat data in landscape analysis. Despite the stochastic nature of SOM, the results in this particular study are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible for the same application with consistent results.