Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

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A Rapid UAV Image Georeference Algorithm Developed for Emergency Response

Journal of Sensors ◽

10.1155/2018/8617843 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Shumin Wang ◽

Ling Ding ◽

Zihan Chen ◽

Aixia Dou

Keyword(s):

Feature Matching ◽

Image Data ◽

Image Rotation ◽

3D Model Reconstruction ◽

The World ◽

Aerial Vehicle ◽

Left Corner ◽

Uav Images ◽

Uav Image ◽

Image Collection

The image collection system based on the unmanned aerial vehicle plays an important role in the postearthquake response and disaster investigation. In the postearthquake response period, for hundreds of image stitching or 3D model reconstruction, the traditional UAV image processing methods may take one or several hours, which need to be improved on the efficiency. To solve this problem, the UAV image rapid georeference method for postearthquake is proposed in this paper. Firstly, we discuss the rapid georeference model of UAV images and then adopt the world file designed and developed by ESRI to organize the georeferenced image data. Next, the direct georeference method based on the position and attitude data collected by the autopilot system is employed to compute the upper-left corner coordinates of the georeferenced images. For the differences of image rotation manners between the rapid georeference model and the world file, the rapid georeference error compensation model from the image rotation is considered in this paper. Finally, feature extraction and feature matching for UAV images and referenced image are used to improve the accuracy of the position parameters in the world file, which will reduce the systematic error of the georeferenced images. We use the UAV images collected from Danling County and Beichuan County, Sichuan Province, to implement the rapid georeference experiments employing different types of UAV. All the images are georeferenced within three minutes. The results show that the algorithm proposed in this paper satisfies the time and accuracy requirements of postearthquake response, which has an important application value.

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Research on application of Unmanned Aerial Vehicles (UAVs) in cadastral mapping of arable land

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2020.61(5).05 ◽

2020 ◽

Vol 61 (5) ◽

pp. 43-53

Author(s):

Quy Ngoc Bui ◽

Tuan Anh Pham ◽

Quan Anh Duong ◽

Hiep Van Pham ◽

Kien Trung Tran ◽

...

Keyword(s):

Agricultural Land ◽

Arable Land ◽

Image Data ◽

Land Administration ◽

Cadastral Maps ◽

Average Accuracy ◽

Land Surveying ◽

Uav Images ◽

Uav Image ◽

Research On Application

Cadastral maps are an important part of cadastral documents, they are legal component of land administration in local authorities. Traditionally, a cadastral map is established by using land surveying methods which can provide high accuracy as required. In recent years, the UAV devices are developed and can provide an accurately tool for cadastral mapping on arable lands. This paper presents an evaluation of UAV application in cadastral mapping in comparison with traditional surveying for arable land. The results show that using UAV images in the mapping of agricultural land can achieve ground accuracy of 1,7 cm and height accuracy of 0,6 cm; In addition, when comparing the average accuracy of the 30 plot vertices and the mean lengths from 29 pairs of edges between the newly created map from the UAV image data and the map provided by the Department of Natural Resources and Environment of Phu Tho province, respectively is: 0,181 m and: 0,051 m.

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A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽

10.3390/s18113921 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3921 ◽

Cited By ~ 17

Author(s):

Wuttichai Boonpook ◽

Yumin Tan ◽

Yinghua Ye ◽

Peerapong Torteeka ◽

Kritanai Torsri ◽

...

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Semantic Segmentation ◽

Water Levels ◽

Building Detection ◽

Network Training ◽

Building Information ◽

Open Standard ◽

Uav Images ◽

Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

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A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-111-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 111-117

Author(s):

Ping Wang ◽

Zheng Wei ◽

Weihong Cui ◽

Zhiyong Lin

Keyword(s):

Image Segmentation ◽

Coastal Area ◽

Minimum Spanning Tree ◽

Edge Weight ◽

Segmentation Method ◽

Optimal Criterion ◽

The Rich ◽

Uav Images ◽

Uav Image ◽

Scale Control

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

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A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas

Remote Sensing ◽

10.3390/rs12101571 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1571

Author(s):

Fan Zhang ◽

Zhenqi Hu ◽

Yaokun Fu ◽

Kun Yang ◽

Qunying Wu ◽

...

Keyword(s):

Machine Learning ◽

Coal Mining ◽

High Precision ◽

Machine Learning Algorithms ◽

Support Vector ◽

Surface Cracks ◽

Mining Areas ◽

Identification Method ◽

Uav Images ◽

Uav Image

Obtaining real-time, objective, and high-precision distribution information of surface cracks in mining areas is the first task for studying the development regularity of surface cracks and evaluating the risk. The complex geological environment in the mining area leads to low accuracy and efficiency of the existing extracting cracks methods from unmanned air vehicle (UAV) images. Therefore, this manuscript proposes a new identification method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small sub-images, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered Vegetation, and Green Vegetation. Then, for each dataset, a training sample is established with cracks and no cracks as labels and the RGB (red, green, and blue) three-band value of the sub-image as feature. Finally, the best machine learning algorithms, dimensionality reduction methods and image processing techniques are obtained through comparative analysis. The results show that using the V-SVM (Support vector machine with V as penalty function) machine learning algorithm, principal component analysis (PCA) to reduce the full features to 95% of the original variance, and image color enhancement by Laplace sharpening, the overall accuracy could reach 88.99%. This proves that the method proposed in this manuscript can achieve high-precision crack extraction from UAV image.

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Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire

Forests ◽

10.3390/f10111025 ◽

2019 ◽

Vol 10 (11) ◽

pp. 1025 ◽

Cited By ~ 8

Author(s):

Jung-il Shin ◽

Won-woo Seo ◽

Taejung Kim ◽

Joowon Park ◽

Choong-shik Woo

Keyword(s):

Forest Fire ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accurate Method ◽

Burn Severity ◽

Spectral Angle Mapper ◽

Before And After ◽

Uav Images ◽

Uav Image

Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future.

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Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Sensors ◽

10.3390/s20072007 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2007

Author(s):

Ruizhe Shao ◽

Chun Du ◽

Hao Chen ◽

Jun Li

Keyword(s):

Image Stitching ◽

Feature Points ◽

Point Matching ◽

Novel Approach ◽

Aerial Vehicle ◽

Speed Up ◽

Anchor Points ◽

Uav Images ◽

Uav Image ◽

Traditional Approaches

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.

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Detection of Flood Damage in Urban Residential Areas Using Object-Oriented UAV Image Analysis Coupled with Tree-Based Classifiers

Remote Sensing ◽

10.3390/rs13193913 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3913

Author(s):

Joanna Zawadzka ◽

Ian Truckell ◽

Abdou Khouakhi ◽

Mónica Rivas Casado

Keyword(s):

Object Oriented ◽

Flood Damage ◽

Visual Interpretation ◽

Residential Areas ◽

Residential Housing ◽

High Resolution Imagery ◽

Improve Accuracy ◽

Uav Images ◽

Uav Image ◽

Current Stage

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.

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A New Dynamic Programming Method for UAV Image Stitching Using Optical Flow

10.20944/preprints201801.0129.v1 ◽

2018 ◽

Author(s):

Weilong Zhang ◽

Bingxuan Guo ◽

Ming Li ◽

Xuan Liao ◽

Yuan Yao ◽

...

Keyword(s):

Dynamic Programming ◽

Optical Flow ◽

Dynamic Programming Algorithm ◽

Programming Algorithm ◽

Dynamic Programming Method ◽

Image Stitching ◽

Dual Channel ◽

New Energy ◽

Uav Images ◽

Uav Image

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new dynamic programming for seamlessly stitching UAV images using optical flow. Our solution consists of two steps: Firstly, an image-matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is develop based on the concept of a stereo dual-channel energy accumulation using optical flow. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for adjacent image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching. Beyond that, our solution is also direction-independent, which has more adaptability and robustness for UAV images.

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