scholarly journals Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks

2019 ◽  
Vol 11 (9) ◽  
pp. 1040 ◽  
Author(s):  
Haiqing He ◽  
Junchao Zhou ◽  
Min Chen ◽  
Ting Chen ◽  
Dajun Li ◽  
...  
Keyword(s):  
Rural Areas ◽  
Point Clouds ◽  
Feature Representation ◽  
Surface Model ◽  
Building Detection ◽  
Building Extraction ◽  
Superpixel Segmentation ◽  
Convolutional Networks ◽  
3D Point Clouds ◽  
Uav Images

Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this area and the significant progress which has been achieved in the past decade. This paper presents an effective approach to extracting buildings from Unmanned Aerial Vehicle (UAV) images through the incorporation of superpixel segmentation and semantic recognition. A framework for building extraction is constructed by jointly using an improved Simple Linear Iterative Clustering (SLIC) algorithm and Multiscale Siamese Convolutional Networks (MSCNs). The SLIC algorithm, improved by additionally imposing a digital surface model for superpixel segmentation, namely 6D-SLIC, is suited for building boundary detection under building and image backgrounds with similar radiometric signatures. The proposed MSCNs, including a feature learning network and a binary decision network, are used to automatically learn a multiscale hierarchical feature representation and detect building objects under various complex backgrounds. In addition, a gamma-transform green leaf index is proposed to truncate vegetation superpixels for further processing to improve the robustness and efficiency of building detection, the Douglas–Peucker algorithm and iterative optimization are used to eliminate jagged details generated from small structures as a result of superpixel segmentation. In the experiments, the UAV datasets, including many buildings in urban and rural areas with irregular shapes and different heights and that are obscured by trees, are collected to evaluate the proposed method. The experimental results based on the qualitative and quantitative measures confirm the effectiveness and high accuracy of the proposed framework relative to the digitized results. The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

scholarly journals DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 1005-1009 ◽  
Author(s):  
S. Rhee ◽  
T. Kim
Keyword(s):  
Global Optimization ◽  
Image Matching ◽  
Point Clouds ◽  
Image Space ◽  
Object Space ◽  
3D Point Clouds ◽  
Local Point ◽  
Matching Technique ◽  
Uav Images ◽  
Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

scholarly journals Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

2020 ◽  
Vol 12 (15) ◽  
pp. 2397 ◽  
Author(s):  
Aletta Dóra Schlosser ◽  
Gergely Szabó ◽  
László Bertalan ◽  
Zsolt Varga ◽  
Péter Enyedi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Clouds ◽  
Recursive Feature Elimination ◽  
Building Detection ◽  
Building Extraction ◽  
Monitoring Methods ◽  
Aerial Surveys ◽  
Building Monitoring ◽  
Reliable Monitoring ◽  
Dense Point

Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

scholarly journals Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

2019 ◽  
Vol 11 (10) ◽  
pp. 1204 ◽  
Author(s):  
Yue Pan ◽  
Yiqing Dong ◽  
Dalei Wang ◽  
Airong Chen ◽  
Zhen Ye
Keyword(s):  
Classification Tree ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Surface Model ◽  
Reconstruction Method ◽  
3D Point Clouds ◽  
Surface Models ◽  
Structural Surface ◽  
Better Than

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

scholarly journals Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

Drones ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Ryan G. Howell ◽  
Ryan R. Jensen ◽  
Steven L. Petersen ◽  
Randy T. Larsen
Keyword(s):  
Data Storage ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Sage Grouse ◽  
Double Pass ◽  
Single Pass ◽  
3D Point Clouds ◽  
Aerial Systems

In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time.

scholarly journals DEVELOPMENT OF AN ALL-PURPOSE FREE PHOTOGRAMMETRIC TOOL

2016 ◽  
Vol XLI-B6 ◽  
pp. 31-38 ◽  
Author(s):  
D. González-Aguilera ◽  
L. López-Fernández ◽  
P. Rodriguez-Gonzalvez ◽  
D. Guerrero ◽  
D. Hernandez-Lopez ◽  
...  
Keyword(s):  
Computer Vision ◽  
Point Clouds ◽  
Educational Needs ◽  
High Accuracy ◽  
System Evaluation ◽  
Improve Quality ◽  
3D Point Clouds ◽  
Uav Images ◽  
Different Levels ◽  
Educational Component

Photogrammetry is currently facing some challenges and changes mainly related to automation, ubiquitous processing and variety of applications. Within an ISPRS Scientific Initiative a team of researchers from USAL, UCLM, FBK and UNIBO have developed an open photogrammetric tool, called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS allows to obtain dense and metric 3D point clouds from terrestrial and UAV images. It encloses robust photogrammetric and computer vision algorithms with the following aims: (i) increase automation, allowing to get dense 3D point clouds through a friendly and easy-to-use interface; (ii) increase flexibility, working with any type of images, scenarios and cameras; (iii) improve quality, guaranteeing high accuracy and resolution; (iv) preserve photogrammetric reliability and repeatability. Last but not least, GRAPHOS has also an educational component reinforced with some didactical explanations about algorithms and their performance. The developments were carried out at different levels: GUI realization, image pre-processing, photogrammetric processing with weight parameters, dataset creation and system evaluation. <br><br> The paper will present in detail the developments of GRAPHOS with all its photogrammetric components and the evaluation analyses based on various image datasets. GRAPHOS is distributed for free for research and educational needs.

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

scholarly journals DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds

2021 ◽  
Vol 13 (17) ◽  
pp. 3484
Author(s):  
Jie Wan ◽  
Zhong Xie ◽  
Yongyang Xu ◽  
Ziyin Zeng ◽  
Ding Yuan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Long Range ◽  
Point Clouds ◽  
Feature Representation ◽  
Learning Ability ◽  
Geometric Feature ◽  
Cloud Data ◽  
Fine Grained ◽  
3D Point Clouds ◽  
Benchmark Datasets

Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and unordered structure in a 3D space. To alleviate this problem, a Dilated Graph Attention-based Network (DGANet) with a certain feature for learning ability is proposed. Specifically, we first build a local dilated graph-like region for each input point to establish the long-range spatial correlation towards its corresponding neighbors, which allows the proposed network to access a wider range of geometric information of local points with their long-range dependencies. Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset–attention mechanism, the proposed network promises to highlight the differing ability of each edge of the constructed local graph to uniquely learn the discrepancy feature of geometric attributes between the connected point pairs. Finally, all the learned edge attention features are further aggregated, allowing the most significant geometric feature representation of local regions by the graph–attention pooling to fully extract local detailed features for each point. The validation experiments using two challenging benchmark datasets demonstrate the effectiveness and powerful generation ability of our proposed DGANet in both 3D object classification and segmentation tasks.

scholarly journals Can 3D Point Clouds Replace GCPs?

2014 ◽  
Vol II-5 ◽  
pp. 347-354 ◽  
Author(s):  
G. Stavropoulou ◽  
G. Tzovla ◽  
A. Georgopoulos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Field Work ◽  
Surface Model ◽  
Control Points ◽  
3D Point Clouds ◽  
Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

scholarly journals Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

2021 ◽  
Vol 7 (2) ◽  
pp. 57-74
Author(s):  
Lamyaa Gamal EL-Deen Taha ◽  
A. I. Ramzi ◽  
A. Syarawi ◽  
A. Bekheet
Keyword(s):  
Neural Network ◽  
Digital Camera ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Surface Model ◽  
Digital Surface Model ◽  
3D Point Clouds ◽  
Digital Elevation ◽  
Surface Models ◽  
Elevation Model

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images.  In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used.  Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images. 

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