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| 1 | Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests显示文摘There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map. | Andrei DORNIK Lucian DRAGUT Petru URDEA | 2018 | Pedosphere2018，28，6： | 4 |
| 2 | A hirearchical approach to model web query interfaces for web source integration显示文摘 | Dragut E C Kabisch T Yu C | 2009 | Journal Proceedings of the VLDB Endowment2009，2，1： | 1 |
| 3 | A Monotonic On-Line Linear Algorithm for Hierarchical Agglomerative Classification显示文摘 | Andreea B. Dragut Codrin M. Nichitiu | 2004 | Information Technology and Management (-)2004，，1： | 1 |
| 4 | Automated classification of landform elements using object - based image analysis显示文摘 | DRAGUT L BLASCHKE T | 2006 | Geomorphology2006，，81： | 1 |
| 5 | Automated parameterizationfor multi-scale image segmentation on multiple layers显示文摘 | Dragut L Csillik O Eisank C | 2014 | ISPRS Journal of Photogrammetry and Remote Sensing2014，88，： | 1 |
| 6 | Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling 显示文摘 | Dragut L Schauppenlehner T Muhar A | 2009 | Computers & Geosciences2009，35，： | 1 |
| 7 | ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data显示文摘 | Dragut L Tiede D Levick S R | 2010 | International Journal of Geo- graphical Information Science2010，24，6： | 1 |
| 8 | Automated classification of landform elements using object-based image analysis显示文摘 | Dragut L Blaschke T | 2006 | Geomorphology2006，81，33： | 1 |
| 9 | ESP: a tool to estimate scale pa- rameter for muhiresolution image segmentation of remotely sensed data 显示文摘 | Dragut L Tiede D Levick S R | 2010 | International Journal of Geographical Information Sci- ence2010，24，6： | 1 |
| 10 | Stock Data Clustering and Multiscale Trend Detection 显示文摘 | Dragut A B | 2012 | Methodology and Computing in Applied Probability2012，14，1： | 1 |
| 11 | ESP: A Tool to Estimate Scale Parameters for Muhiresolution Image Segmentation of Remotely Sensed Data显示文摘 | Dragut L Tiede D Levick S | 2010 | Int J Geogr Inf Sci2010，24，6： | 1 |
| 12 | Automated classification of landform elements using object-based image analysis显示文摘 | DRAGUT L BLASCHKE T | 2006 | Geomorphology2006，81，34： | 1 |
| 13 | A hierarchical approach to model Web query interfaces for Web source integration显示文摘 | Kabisch T Dragut E C Yu C T | 2009 | Proc of the Very Large Data Bases Endowment2009，2，1： | 1 |
| 14 | ESP: a tool to estimate scale pa- rameter for multiresolution image segmentation of remotely sensed data显示文摘 | Dragut L Tiede D Levick SR | 2010 | International Journal of Geographical Informa tion Science2010，24，85： | 1 |
| 15 | Automated classification of landform elements using object-based image analysis显示文摘 | Lucian Dragut Thomas Blaschke | 2006 | Geomorphology2006，81，34： | 1 |
| 16 | Automated Classification of Landform Elements Using Object-Based Image Analysis显示文摘 | Dragut L Blaschke T | 2006 | Geomorphology2006，81，： | 1 |
| 17 | A Hierarchical Approach to Model Web Query Interfaces for Web Source Integration显示文摘 | Dragut E C Kabisch T Yu Clement | 2009 | Journal of Very Large Database2009，2，1： | 1 |
| 18 | 基于面向对象的地貌自动分类显示文摘本文介绍了基于面向对象的地貌自动分类系统。首先,由数字地形模型生成高程、坡度、剖面曲率和平面曲率;其次,通过图像分割将同类对象分为多个级别。依据分类模型(建立在地表形态和对象高程之上的分类模型)将初始分割对象划分为不同的地貌类型。截至目前,坡向信息还未在分类中使用。该分类系统共有9个地貌类型:山顶和坡脚(由高程位置和显性度来定义),陡坡、平坦区和缓坡区(由坡度值来定义),肩坡和负向坡(由剖面曲率来定义),头坡、侧坡和鼻坡(由平面曲率来定义)。分类采用灵活的模糊隶属函数确定。将分类结果叠加在DTMs上进行分析,用特定的模糊分类选项对分类结果进行了精度评价。该方法在罗马尼亚和德国的Berchtesgaden国家公园两个区域作了对比研究,证明是可重复的,且容易适应不同的自然景观和数据集,可以为地貌和景观研究提供有用信息。该分类方法的主要优势在于它容易推广使用,因为该方法只使用相对值和相对位置,几乎可以用于所有探讨地形特征和其他地貌组分相关关系的领域。 | Lucian Dragut Thomas Blaschke 黄文星 杨丽娟 | 2014 | 海洋地质2014，，1： | 0 |
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