scholarly journals A Building Detection Method Based on Semi-suppressed Fuzzy C-means and Restricted Region Growing Using Airborne LiDAR

2019 ◽  
Vol 11 (7) ◽  
pp. 848 ◽  
Author(s):  
Zhan Cai ◽  
Hongchao Ma ◽  
Liang Zhang
Keyword(s):  
Detection Method ◽  
Three Dimensional ◽  
Region Growing ◽  
Airborne Lidar ◽  
Building Detection ◽  
Small Areas ◽  
Fuzzy C Means ◽  
Restricted Region ◽  
Essential Prerequisite ◽  
Coarse To Fine

Building detection using airborne Light Detection And Ranging (LiDAR) data is the essential prerequisite of many applications, including three-dimensional city modeling. In the paper, we propose a coarse-to-fine building detection method that is based on semi-suppressed fuzzy C-means and restricted region growing. Based on a filtering step, the remaining points can be separated into two groups by semi-suppressed fuzzy C-means. The group contains points that are located on building roofs that form a building candidate set. Subsequently, a restricted region growing algorithm is implemented to search for more building points. The proposed region growing method perfectly ensures the rapid growth of building regions and slow growth of non-building regions, which enlarges the area differences between building and non-building regions. A two-stage strategy is then adopted to remove tiny point clusters with small areas. Finally, a minimum bounding rectangle (MBR) is used to supplement the building points and refine the results of building detection. Experimental results on five datasets, including three datasets that were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and two Chinese datasets, verify that most buildings and non-buildings can be well separated during our coarse building detection process. In addition, after refined processing, our proposed method can offer a high success rate for building detection, with over 89.5% completeness and a minimum 91% correctness. Hence, various applications can exploit our proposed method.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

Urban energetics applications and Geomatic technologies in a Smart City perspective

2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici
Keyword(s):  
Smart City ◽  
Laser Scanning ◽  
Near Infrared ◽  
Three Dimensional ◽  
Energy Sector ◽  
Primary Objective ◽  
Airborne Lidar ◽  
City Management ◽  
Urban Context ◽  
Research Areas

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

scholarly journals A Novel Coarse-to-fine Sea-land Segmentation Technique Based on Superpixel Fuzzy C-Means Clustering and Modified Chan-Vese Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Eman Elkhateeb ◽  
Hassan Soliman ◽  
Ahmed Atwan ◽  
Mohammed Elmogy ◽  
Kyung-Sup Kwak ◽  
...  
Keyword(s):  
Fuzzy C Means ◽  
Segmentation Technique ◽  
Fuzzy C Means Clustering ◽  
Coarse To Fine

scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

Three-dimensional crystallization of membrane proteins

1988 ◽  
Vol 21 (4) ◽  
pp. 429-477 ◽  
Author(s):  
W. Kühlbrandt
Keyword(s):  
High Resolution ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Biological Macromolecules ◽  
X Ray ◽  
X Ray Crystallography ◽  
Membrane Protein Complexes ◽  
Essential Prerequisite

As recently as 10 years ago, the prospect of solving the structure of any membrane protein by X-ray crystallography seemed remote. Since then, the threedimensional (3-D) structures of two membrane protein complexes, the bacterial photosynthetic reaction centres of Rhodopseudomonas viridis (Deisenhofer et al. 1984, 1985) and of Rhodobacter sphaeroides (Allen et al. 1986, 1987 a, 6; Chang et al. 1986) have been determined at high resolution. This astonishing progress would not have been possible without the pioneering work of Michel and Garavito who first succeeded in growing 3-D crystals of the membrane proteins bacteriorhodopsin (Michel & Oesterhelt, 1980) and matrix porin (Garavito & Rosenbusch, 1980). X-ray crystallography is still the only routine method for determining the 3-D structures of biological macromolecules at high resolution and well-ordered 3-D crystals of sufficient size are the essential prerequisite.

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