1,721,291 research outputs found
Experimenting a robust surface texture operator in Alpine environment for high resolution geomorphometry
No full textIn relation to the meeting session "Learning from spatial data: rappresentazione, analisi e processi geo-ambientali" (Learning from data: representation, analysis and geo-environmental processes) we presented a geomorphometric analysis via ad-hoc developed robust surface texture operators, specifically designed for the analysis of high resolution DTMs (HRDTM); in particular we presented some comparison between our proposed surface texture operator and a classical operator based on variogram. The possibility to conduct a robust analysis on noisy and highly non-stationary spatial data, such the one represented by high resolution topography, is particularly interesting in the geomorphological and geo-engineering contexts; also, the usage of surface texture indexes can reveal important information on the typology and intensities of the geomorphic processes and factors
MAD : robust image texture analysis for applications in high resolution geomorphometry
No full textThe analysis of surface textures plays an important role in the geomorphometric analysis of high-resolution digital terrain models. Surface textures can be analyzed by means of geostatistical variogram-based indices. The use of variogram-based indices is promising because of their ability to consider the multiscale and anisotropic character of morphometric data. However, similar to other variance-type statistics, variogram-based indices are sensitive to the presence of hotspots and non-stationary data. Consequently, we present a multi-scale and directional image texture analysis operator (MAD or Median Absolute Differences) derived from a modification of a variogram estimator. MAD has been specifically developed to improve the robustness of variogram-based surface indices with a special focus on strongly non-stationary and often noisy spatial data representing solid earth surface morphology. Although the operator has been specifically developed for the analysis of high-resolution digital terrain models, it can be applied to the texture analysis of any type of image. Consequently MAD could be of interest in the broader context of remote sensing as well as for all disciplines for which image texture analysis is relevant. The theoretical presentation of the surface texture operator is accompanied by a working software prototype. The software prototype has been implemented in the Python scripting language for use in ArcGIS (ESRI) using its Spatial Analyst functions. The prototype architecture is concise and can be easily coded in different software environments, such as GIS mapping and image analysis software. The software prototype proposed has been developed to facilitate the development of ad hoc surface texture indices capable of adapting to the special needs of the study at hand. The MAD operator represents an improvement over variogram-based surface texture indices, offering a robust description of relevant aspects of surface texture, including surface roughness
Roughness-based landscape segmentation via fuzzy clustering: potentials of a robust surface texture estimator
No full textIn this study, the impact of robust surface texture indices on the unsupervised morphological segmentation of an alpine basin is explored. Roughness indices calculated on a high resolution digital terrain model derived by means of airborne LiDAR (Light Detection and Ranging) are used as input features in the clustering procedure. The segmentation in textural classes is based on a fuzzy clustering approach, permitting the optimal selection of the number of clusters and fuzziness of the classification. A comparison is made between clusters derived from variogram-based indices and MAD?based (median absolute differences) roughness indices within a small alpine basin. As expected, with both approaches, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes. However, the segmentation derived using MAD-based roughness indices shows relevant improvements; in particular, the spatial patterns of the MAD-based classification show less artifacts respect to the variogram-based one
Geomorphometric analysis of fine-scale morphology for extensive areas: a new surface-texture operator
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