Geomorphometric feature analysis using morphometric parameterization and artificial neural networks

This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detai ...

[1]  Steven E. Franklin,et al.  An automated approach to the classification of the slope units using digital data , 1998 .

[2]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  R. J. Pike,et al.  Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature , 2007 .

[4]  Peter L. Guth,et al.  Geomorphometry from SRTM: Comparison to NED , 2006 .

[5]  John R. G. Townshend,et al.  Terrain Analysis and Remote Sensing , 1981 .

[6]  Stephen J. Ventura,et al.  Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin , 1997 .

[7]  Peter A. Burrough,et al.  High-resolution landform classification using fuzzy k-means , 2000, Fuzzy Sets Syst..

[8]  Richard J. Chorley Spatial analysis in geomorphology , 1972 .

[9]  A. Roth,et al.  The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar , 2003 .

[10]  Zhe Li,et al.  The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self‐Organizing Map for Supervised Classification , 2006, Trans. GIS.

[11]  Ricardo Vilalta,et al.  Digital topography models for Martian surfaces , 2005, IEEE Geoscience and Remote Sensing Letters.

[12]  Robert Wright,et al.  An assessment of shuttle radar topography mission digital elevation data for studies of volcano morphology , 2006 .

[13]  Yuri Gorokhovich,et al.  Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics , 2006 .

[14]  Anthony J. Richardson,et al.  Using self-organizing maps to identify patterns in satellite imagery , 2003 .

[15]  A. Bolongaro-Crevenna,et al.  Geomorphometric analysis for characterizing landforms in Morelos State, Mexico , 2005 .

[16]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[17]  Ma Jianwen,et al.  Land-use classification using ASTER data and self-organized neutral networks , 2005 .

[18]  Kevin Amaratunga,et al.  Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission , 2005 .

[19]  Fernando Bação,et al.  The self-organizing map, the Geo-SOM, and relevant variants for geosciences , 2005, Comput. Geosci..

[20]  Federico Marini,et al.  Class-modeling using Kohonen artificial neural networks , 2005 .

[21]  Jonathan Raper,et al.  Three dimensional applications in Geographical Information Systems , 1989 .

[22]  Ralf Ludwig,et al.  Validation of digital elevation models from SRTM X-SAR for applications in hydrologic modeling , 2006 .

[23]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[24]  Kosmas Pavlopoulos,et al.  Computer-assisted discrimination of morphological units on north-central Crete (Greece) by applying multivariate statistics to local relief gradients , 2004 .

[25]  Brian D. Bue,et al.  Automated classification of landforms on Mars , 2006, Comput. Geosci..

[26]  Tomasz F. Stepinski,et al.  Extraction of Martian valley networks from digital topography , 2004 .

[27]  Alan M. MacEachren,et al.  Evaluating the usability of visualization methods in an exploratory geovisualization environment , 2006, Int. J. Geogr. Inf. Sci..

[28]  Tomislav Hengl,et al.  Supervised Landform Classification to Enhance and Replace Photo‐Interpretation in Semi‐Detailed Soil Survey , 2003 .

[29]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[30]  Dan G. Blumberg,et al.  Analysis of large aeolian (wind-blown) bedforms using the Shuttle Radar Topography Mission (SRTM) digital elevation data , 2006 .

[31]  G. Miliaresis,et al.  Vertical accuracy of the SRTM DTED level 1 of Crete , 2005 .

[32]  Carlos Henrique Grohmann,et al.  SRTM-based morphotectonic analysis of the Poços de Caldas Alkaline Massif, southeastern Brazil , 2007, Comput. Geosci..

[33]  Holger Gärtner,et al.  Principles of semantic modeling of landform structures , 2001 .

[34]  John D. Vona,et al.  Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets , 2004 .

[35]  G. Hancock,et al.  A comparison of SRTM and high‐resolution digital elevation models and their use in catchment geomorphology and hydrology: Australian examples , 2006 .

[36]  R. Purves,et al.  Tectonic forcing of longitudinal valleys in the Himalaya: morphological analysis of the Ladakh Batholith, North India , 2004 .

[37]  A. C. Seijmonsbergen,et al.  Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM , 2006 .

[38]  Sueli Aparecida Mingoti,et al.  Comparing SOM neural network with Fuzzy c , 2006, Eur. J. Oper. Res..

[39]  R. Yokoyama,et al.  Supervised landform classification of Northeast Honshu from DEM-derived thematic maps , 2006 .

[40]  Richard J. Pike,et al.  Geomorphometry -diversity in quantitative surface analysis , 2000 .

[41]  Jo Wood,et al.  Where is Helvellyn? Fuzziness of multi‐scale landscape morphometry , 2004 .

[42]  G. Miliaresis Geomorphometric mapping of Zagros Ranges at regional scale , 2001 .

[43]  M. Canty,et al.  Unsupervised classification of satellite imagery: Choosing a good algorithm , 2002 .

[44]  Daniel G. Brown,et al.  Supervised classification of types of glaciated landscapes using digital elevation data , 1998 .