Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China
Authors
Keywords
DEM data, image texture, feature extraction, Gray Level Co-occurrence Matrix (GLCM), optimal parametric analysis, landform classification
Journal
Frontiers of Earth Science
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2019-08-10
DOI
10.1007/s11707-019-0751-2
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Stability analysis unit and spatial distribution pattern of the terrain texture in the northern Shaanxi Loess Plateau
- (2018) Hu Ding et al. Journal of Mountain Science
- Scale characters analysis for gully structure in the watersheds of loess landforms based on digital elevation models
- (2018) Hongchun Zhu et al. Frontiers of Earth Science
- Geological Mapping in Western Tasmania Using Radar and Random Forests
- (2018) Declan D. G. Radford et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Monitoring the Morphological Transformation of Beijing Old City Using Remote Sensing Texture Analysis
- (2017) Antoine Lefebvre et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales
- (2017) Mryka Hall-Beyer INTERNATIONAL JOURNAL OF REMOTE SENSING
- Chinese progress in geomorphometry
- (2017) Guonian Lv et al. Journal of Geographical Sciences
- Automatic recognition of loess landforms using Random Forest method
- (2017) Wu-fan Zhao et al. Journal of Mountain Science
- Loess terrain segmentation from digital elevation models based on the region growth method
- (2017) Hongchun Zhu et al. PHYSICAL GEOGRAPHY
- Classification of landforms in Southern Portugal (Ria Formosa Basin)
- (2015) Fernando M. G. Martins et al. Journal of Maps
- Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix
- (2013) Isabelle Champion et al. IEEE Geoscience and Remote Sensing Letters
- Geographic Object-Based Image Analysis – Towards a new paradigm
- (2013) Thomas Blaschke et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery
- (2013) Minho Kim et al. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
- Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach
- (2012) Maher Arebey et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
- (2012) V.F. Rodriguez-Galiano et al. REMOTE SENSING OF ENVIRONMENT
- Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution
- (2011) Tetsuji Ota et al. Journal of Forest Research
- EEG signal classification using PCA, ICA, LDA and support vector machines
- (2010) Abdulhamit Subasi et al. EXPERT SYSTEMS WITH APPLICATIONS
- A kernel functions analysis for support vector machines for land cover classification
- (2009) T. Kavzoglu et al. International Journal of Applied Earth Observation and Geoinformation
- Applications of remote sensing in geomorphology
- (2009) M.J. Smith et al. PROGRESS IN PHYSICAL GEOGRAPHY
- Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery
- (2008) Yashon O. Ouma et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now