Damage classification of pipelines under a water flow operation using a multi-scale actuated sensing technology

2011 ◽  
Author(s):  
Chang-Gil Lee ◽  
Seunhee Park
Keyword(s):  
Water Flow ◽  
Damage Classification ◽  
Multi Scale ◽  
Sensing Technology

Probabilistic Urban Structural Damage Classification Using Bitemporal Satellite Images

2010 ◽  
Vol 26 (1) ◽  
pp. 87-109 ◽  
Author(s):  
ZhiQiang Chen ◽  
Tara C. Hutchinson
Keyword(s):  
Structural Damage ◽  
Satellite Images ◽  
Probabilistic Approach ◽  
Damage Classification ◽  
Classification Framework ◽  
Sensing Technology ◽  
Probabilistic Classification ◽  
Multiclass Classifier ◽  
Post Disaster

Recent research endeavors in civil engineering have attempted to apply remote sensing technology to urban damage assessment as an aid for post-disaster reconnaissance and recovery. In these attempts, urban structural damage is identified based on pre- and post-disaster satellite images with the use of a pattern classification approach. The result is usually presented in a damage map wherein categorical damage levels, such as “fully collapsed,” “partially collapsed,” or “intact,” are assigned to urban subregions or individual structures in images. However, a major limitation in past attempts is the use of deterministic approaches to classify damage levels. In general, these approaches are not able to capture the inherent uncertainties of structural damage and lack scalability when analyzing damage to built urban subregions of different sizes. To address this, a probabilistic classification framework by means of a multiclass classifier is proposed. By applying this probabilistic approach, classification of urban damage provides posterior probabilities, which can be used to quantify decision uncertainties and to obtain regional urban damage classification. Numerical experiments are conducted using satellite images acquired from a recent earthquake and a tsunami event, namely the 2003 Bam, Iran Earthquake, and the 2004 India Ocean Tsunami.

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Multi-scale superpixel spectral–spatial classification of hyperspectral images

2016 ◽  
Vol 37 (20) ◽  
pp. 4905-4922 ◽  
Author(s):  
Shanshan Li ◽  
Li Ni ◽  
Xiuping Jia ◽  
Lianru Gao ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Hyperspectral Images ◽  
Spatial Classification ◽  
Multi Scale

Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet

2019 ◽  
Vol 56 (5) ◽  
pp. 052804
Author(s):  
赵中阳 Zhao Zhongyang ◽  
程英蕾 Cheng Yinglei ◽  
释小松 Shi Xiaosong ◽  
秦先祥 Qin Xianxiang ◽  
李鑫 Li Xin
Keyword(s):  
Point Cloud ◽  
Terrain Classification ◽  
Multi Scale
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