Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Automated Landform Classification of China Based on Hammond’s Method

The automatic classification of Macro landforms was processed with the program developed by Hammond’s Manual procedures, which based on properties of slope, local relief, and profile type, which consists of 5 landform types, 24 landform class and 96 landform subclasses. This program identified landform types by moving a square window with size of 9.8 km × 9.8 km. The data includes 816 sheets of topological map with a scale of 1:250,000. The DEM were buildup with the contours and mark points based on this data with a cell size of 200 m, and merge into one sheet. The automated classification was processed on this DEM data with a AML program of ArcGIS 10.X Workstation. The result indicates it produced a classification that has good resemblance to the landforms in China. The maps were produced respectively with 5 types, 16 classes and, 90 subclasses The 5 Landform types of landforms were Plains (PLA), 20.25% of whole areas;Tablelands (TAB) of 3.56%;Plains with Hills or Mountains (PHM) of 32.84%;Open Hills and Mountains (OHM) of 18.72%;Hills and Mountains (HM) of 24.63%. In the result of 24 landform classes, there are not some classes, such as irregular plains with low relief;open very low hills, open low hills;very low hills, low hills, moderate hills. The result of 96 landform subclass is similar to the 24 class.

基于SRTM DEM与变点分析法的云南省富宁县地貌形态研究

以位于云贵高原至广西丘陵倾斜面上的云南省富宁县为研究区，提出了适合研究区地形特点的地貌形态分类指标体系；基于 SRTM DEM 90 m 分辨率的地形数据，用均值变点分析法，确定8像元×8像元（0．5184 km^2）的格网为该县地形起伏度的最佳统计单元，据此提取了该县地形起伏度（0～707 m）；最后，叠加分析了该县绝对海拔和地形起伏度数据，得到12种基本地貌形态，并得出结论：小起伏较低山、小起伏中山是该县最主要的地貌形态。

Deep learning of DEM image texture for landform classification in the Shandong area,China

Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient.

Double Polarization SAR Image Classification based on Object-Oriented Technology

This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.

Landform Planation Index Extracted from DEMs： A Case Study in Ordos Platform of China

Planation surface, a surface that is almost flat, is a kind of low-relief landforms. Planation surface is the consequence of the denudation and planation processes under a tectonic stable condition. The quantitative expression of the characteristics of planation surface plays a key role in reconstructing and describing the evolutionary process of landforms. In this study, Landform Planation Index(LPI), a new terrain derivative, was proposed to quantify the characteristics of planation surface. The LPIs were calculated based on the summit surfaces formed according to the clustering results of peaks. Ten typical areas in the Ordos Platform located in the central part of the Loess Plateau of China are chosen as the test areas for investigating their planation characteristics with the LPI. The experimental results indicate that the LPI can be effectively used to quantify the characteristics of planation surfaces. In addition, the LPI can be further used to depict the patterns of spatial differentiation in the Ordos Platform. Although the present Ordos Platform area is full of the high-density gullies, its planation characteristics is found to be well preserved. Furthermore, the characteristics of the planation surfaces can also reflect the original morphology of the Ordos Platform before the loess dusts deposition process evolved in this area. The statistical results of the LPI show that there is a gradually increasing tendency along with the increasing of slope gradient of summit surface. It indicates that the characteristics of planation surfaces vary among test areas with different landforms. These findings help to deepen the understanding of planation characteristics of the loess landform and its underlying paleotopography. Results of this study can be also served as an important theoretical reference value for revealing the evolutionary process of loess landform.

一种DEM辅助下的LiDAR点云PTD滤波改进算法

针对传统渐进加密不规则三角网(PTD)滤波算法在复杂地形环境下需要反复调试地面点判断参数才能获得较好结果的局限性,以往期DEM数据提取的地形高程和地形梯度为辅助,改进PTD中初始地面种子点的选取方法,优化地面点判断参数,并对往期DEM数据和现势LiDAR点云数据之间的地形变化进行检测和处理,适用于不同坡度地形条件的复杂地形,滤波效果较好。对比分析实验数据精度可知,该算法能有效降低I类与II类误差,且样本分类精度均在90%以上,说明DEM辅助可切实提高PTD滤波算法的精度。

基于CFD-DEM耦合的动态选粉机机理研究

基于CFD-DEM耦合方法,建立工业级动态选粉机三维数值模型,探究动态选粉机内部详细的颗粒运动轨迹与选粉机理,为后续动态选粉机优化奠定基础。研究结果表明:选粉机转笼外部气流速度低,转笼内部气流速度高,这给颗粒一个向转笼内部运动的加速度。大直径颗粒质量与离心力大、加速慢,在叶片打到颗粒前,粗颗粒的速度不足以穿过动叶片。反之细颗粒质量与离心力小,加速效果明显,有足够的速度穿过动叶片。被转笼阻挡的粗颗粒下落的关键因素是气流带料能力,而不单单靠颗粒自身重力与动能损耗。

基于机载LiDAR技术的植被密集区域DEM生成方法研究

植被密集区域由于遮挡严重、人员难涉足,利用传统的GPS-RTK测量手段进行数字高程模型(DEM)测绘时存在工作量大、成本高、成果精度低等问题。以某实际工程为例,应用机载LiDAR建立植被密集区域DEM生成方法,对外业LiDAR点云数据采集、点云自动分类及人工分类流程进行分析。结果表明,该技术可高效建立困难地区的高质量DEM,对复杂区域数字化测图具有重要的参考价值。

深度学习智能解译支持下的DEM生成方法

当前深度学习技术极大提升了遥感数据的自动化处理能力,针对DSM到DEM生产过程中降高区域提取环节,采用深度学习语义分割的U-Net模型实现了降高区域的自动提取,构建了面向DEM生产的样本分类系统,形成了规范化样本标注技术方法和优化后的DEM制作技术流程,并在DEM生产实践中检验了该方法的实用性。结果表明,在地表景观层次分明、地物可辨性高的场景下,能得到较好的降高区域提取结果,分类精度可达0.952。相较于传统人工勾绘或逐图幅监督分类的降高区域提取方法,深度学习智能解译辅助下的DEM生产效率可整体提高20%~30%,且能确保区域尺度DEM产品的协调一致性,具有重要的实用价值。

Criteria for assessing carbon emissions peaks at provincial level in China

China has pledged to peak carbon emissions by 2030 and neutralize emissions by 2060.There is an urgent need to develop a comprehensive and reliable methodology to judge whether a region has reached its carbon emissions peak(CEP),as well as to schedule and prioritize mitigation activities for different regions.In this study,we developed an approach for identifying the CEP status of 30 provincial areas in China,considering both the carbon emissions trends and the main socioeconomic factors that influence these trends.According to the results of the Mann-Kendall(MK)tests,changes in carbon emissions for the 30 provincial areas can be grouped inlo four clusters:those with significant reductions,marginal reductions,marginal increases,and significant increases.Then,total energy consumption(TEC),the proportion of coal consumption(PCC),the proportion of the urban population(PUP),the proportion of secondary industry(PASP),and per capita GDP(PGDP)were further identified as the main factors influencing carbon emissions,by applying Redundancy analysis(RDA)and Monte Carlo permutation tests.To balance efficacy with fairness,we assigned scores from 1 to 4 to trends in carbon emissions,and the Group Analysis results of the main influencing factors above except for TEC;for TEC,main basis is the relevant assessment results.And finally,according to the actual condition of total scores,provincial areas were assigned to the first,second,third and fourth stage of progress toward CEP,using the method of Natural Breaks(Jenks).Based on the method,differentiated plans should be adopted from the perspective of fair development and emissions reduction efficiency,in accordance with the basic principles of Doing the Best within Capacity and Common but Differentiated Responsibilities.This classification method can also be adopted by other developing countries which have not yet achieved CEP.

融合选择性稀疏采样的细粒度图像分类

常用的细粒度分类方法通过提取局部信息学习细粒度特征,容易忽视周围环境因素影响问题,造成分类精度下降.针对这一问题提出了一个简单有效的框架,称为选择性稀疏采样.通过类峰值响应产生稀疏注意定位有信息的对象部分,根据图像内容选择动态数量的稀疏注意,生成判别性和补充性两个分支进行视觉表示,使得特征部分和全局信息相辅相成.对于容易产生混淆的部分,引入了一个“梯度增强”损失,只关注每个样本的混淆类,为补充性分支提供更多的细节特征.通过实验结果表明,该方法在常用数据集的基准测试中分别达到了88.6%,92.8%和94.8%的精确度,验证了该方法的有效性.

无人机LiDAR点云与无人机影像匹配点云分析比较

随着无人机技术的不断发展,无人机数码航测技术和无人机LiDAR技术在测量领域的应用越来越广泛。为分析无人机LiDAR点云和无人机影像匹配点云2种点云的差异,该文通过对西南某铁路一个测区在同一飞行高度的情况下同时进行无人机数码航摄及无人机LiDAR航摄2种方式航摄。对2种不同的摄影方式获取的点云进行比较,分析出2种方法获取点云在形态表现、滤波分类,以及利用2种点云制作DEM高程精度方面的差异,为实际工程航飞方式的选择提供一个参考。

基于DEM的醴陵市土地利用空间格局分析

地形是土地利用空间格局形成的基础,本文利用遥感影像、土地利用现状图、地形图、地貌图,以及野外实际调查资料,通过建立DEM、地貌分区、高程分级、叠加分析等过程,以地貌分区和高程分级区域为基本空间单元,分析了湖南醴陵市土地利用空间格局及与地形因子的关系。结果表明土地利用类型空间分布具有强烈的区域差异性,各地貌分区与高程分级区域的土地利用组合不同,同一土地利用类型在不同地貌分区和高程区域的分布存在差异;土地利用类型空间分布与海拔高程具有显著的相关性,随海拔高程增加,林地分布比例增大,耕地、园地、城乡居民用地、工矿用地、水域和未利用土地均呈减少趋势。在此基础上,构建了研究区土地利用空间格局的三维景观模型,直观地再现了土地利用空间格局与所处环境的关系,有利于辨识不同地貌部位土地利用的分布规律与差异,可为研究区农业结构调整和土地资源可持续利用提供参考。

基于DEM坡度图制图中坡度分级方法的比较研究

坡度分级是所制作的坡度图具有科学性与实用性的重要前提,各种分级方法一直是坡度分级研究中的重点。将各种坡度分级方法分为一般主观分级法、临界坡度分级法与模式分级法3大类,并以黄土丘陵沟壑区为实验样区,以高精度5 m分辨率的DEM为信息源,提取坡度数据层面。在此基础上,对不同分级方法的特点、适用性及制图效果等进行了比较分析。研究表明:一般主观分级法简单、灵活,但带有一定的主观性及随意性;临界坡度分级法能较好地满足用户的应用目的,但经常忽视了坡度图制图效果;而模式分级法能够较好地揭示地表的坡度组合规律。应根据应用目的、地面起伏特征等来选择合适的坡度分级方法,这样才能得到合理的坡度分级结果,更大程度地满足用户的应用目的。研究结果对指导正确、有效地制作与应用坡度图具有重要意义。

适用于大尺度水文气候模式的DEM洼地填充和平坦区处理的新方法

针对传统的DEM(Digital Elevation Model)洼地和平坦区处理方法存在的速度慢等问题,提出了新的DEM处理方法。新方法由两个新算法组成:其一为DEM快速分位数分类算法,此算法先利用DEM的统计特征计算出分位数,然后分类,其时间复杂度仅为O(N),速度远大于传统分类方法的O(NlogN);其二为对分类后洼地和平坦区处理的新算法,此算法从高程最小的一类开始逐类处理,直到处理结束。用90m DEM对不同方法进行评价的结果表明:新方法在保证结果准确性前提下显著提高了DEM洼地和平坦区的处理效率。而且,新方法除了可将洼地填平之外,还可对洼地增加微小高程,生成无平坦区的DEM。新方法已成功地应用到地形指数的计算及TOPMODEL水文模拟,还适用于基于DEM的大尺度水文气候模拟与分析。

Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing （ISO） cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients （K） as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.