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

Sensors ◽  
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.

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

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.

scholarly journals Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts

2018 ◽  
Vol 7 (9) ◽  
pp. 361 ◽  
Author(s):  
Ming Li ◽  
Deren Li ◽  
Bingxuan Guo ◽  
Lin Li ◽  
Teng Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Function ◽  
Graph Cuts ◽  
Remote Sensing Image ◽  
Existing Problems ◽  
Image Mosaicking ◽  
Target Energy ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

scholarly journals Improved Parallax Image Stitching Algorithm Based on Feature Block

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 348 ◽  
Author(s):  
Huaitao Shi ◽  
Lei Guo ◽  
Shuai Tan ◽  
Gang Li ◽  
Jie Sun
Keyword(s):  
Feature Matching ◽  
Computational Cost ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Shape Distortion ◽  
Scale Invariant ◽  
Point Matching ◽  
Feature Point Matching ◽  
Scale Invariant Feature

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.

scholarly journals A Rapid UAV Image Georeference Algorithm Developed for Emergency Response

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
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.

scholarly journals Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

2019 ◽  
Vol 11 (6) ◽  
pp. 643 ◽  
Author(s):  
Anastasiia Safonova ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
Alexey Rubtsov ◽  
Yuriy Maglinets ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Bark Beetle ◽  
Forest Health ◽  
Tree Damage ◽  
Abies Sibirica ◽  
Polygraphus Proximus ◽  
State Nature Reserve ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).

Improved Vision-Based Algorithm for Unmanned Aerial Vehicles Autonomous Landing

2013 ◽  
Vol 273 ◽  
pp. 560-565 ◽  
Author(s):  
Yun Ji Zhao ◽  
Hai Long Pei
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Feature Points ◽  
Autonomous Landing ◽  
Detection And Tracking ◽  
Aerial Vehicles ◽  
Camshift Algorithm ◽  
Aerial Vehicle ◽  
Speed Up ◽  
Improved Algorithm

In vision-based autonomous landing system of UAV (Unmanned Aerial Vehicle), the efficiency of object detection and tracking will directly affect the control system. An improved algorithm of SURF (Speed Up Robust Features) will resolve the problem which is inefficiency of the SURF algorithm in the autonomous landing system of UAV. The improved algorithm is composed of three steps: first, detect the region of the target using the Camshift algorithm; second, detect the feature points in the region of the above acquired using the SURF algorithm; third, do the matching between the template target and the region of target in frame. The results of experiments and theoretical analysis testify the efficiency of the algorithm.

Discovering Potential Illegal Construction Within Building Roofs from UAV Images Using Semantic Segmentation and Object-Based Change Detection

2021 ◽  
Vol 87 (4) ◽  
pp. 263-271
Author(s):  
Yang Liu ◽  
Yujie Sun ◽  
Shikang Tao ◽  
Min Wang ◽  
Qian Shen ◽  
...  
Keyword(s):  
Change Detection ◽  
Semantic Segmentation ◽  
Small Object ◽  
Detection Scheme ◽  
Building Roof ◽  
Object Based ◽  
Detection Stage ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

A novel potential illegal construction (PIC) detection method by bitemporal unmanned aerial vehicle (UAV ) image comparison (change detection) within building roof areas is proposed. In this method, roofs are first extracted from UAV images using a depth-channel improved UNet model. A two-step change detection scheme is then implemented for PIC detection. In the change detection stage, roofs with appearance, disappearance, and shape changes are first extracted by morphological analysis. Subroof primitives are then obtained by roof-constrained image segmentation within the remaining roof areas, and object-based iteratively reweighted multivariate alteration detection (IR-MAD ) is implemented to extract the small PICs from the subroof primitives. The proposed method organically combines deep learning and object-based image analysis, which can identify entire roof changes and locate small object changes within the roofs. Experiments show that the proposed method has better accuracy compared with the other counterparts, including the original IR-MAD, change vector analysis, and principal components analysis-K-means.

scholarly journals Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Di Zhang ◽  
Feng Pan ◽  
Qi Diao ◽  
Xiaoxue Feng ◽  
Weixing Li ◽  
...  
Keyword(s):  
Aerial Images ◽  
Large Field ◽  
Small Scale ◽  
Agricultural Insurance ◽  
Aerial Vehicle ◽  
Simple Linear Iterative Clustering ◽  
Evaluation Dataset ◽  
Uav Images ◽  
Uav Image ◽  
Candidate Regions

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

scholarly journals Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

2020 ◽  
Vol 12 (12) ◽  
pp. 1994 ◽  
Author(s):  
Xin Luo ◽  
Xiaoyue Tian ◽  
Huijie Zhang ◽  
Weimin Hou ◽  
Geng Leng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Neural Computing ◽  
Detection Capability ◽  
High Processing Speed ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image ◽  
High Processing

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

scholarly journals Monitoring the Damage of Armyworm in Summer Corn by Unmanned Aerial Vehicle Imaging

2020 ◽  
Author(s):  
mingzheng zhang ◽  
Xinsheng Wang ◽  
Jinghao Xue ◽  
Wei Su ◽  
Dehai Zhu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Regional Scale ◽  
Support Vector ◽  
Surface Model ◽  
Automated Identification ◽  
Aerial Vehicle ◽  
Corn Plants ◽  
Uav Images ◽  
Uav Image ◽  
Sentinel 2

Abstract Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifier (MLPC) and Naive Bayes Classifier (NB) classifiers. Furthermore, the incorporation of an Unmanned Aerial Vehicle (UAV) image-generated digital surface model improved the performance of the RF classifier, increasing the F-score from 0.985 and 0.970 to 0.997 and 0.994, and increasing the Kappa coefficient from 0.955 to 0.990. In addition, we found that Band 3 (735 nm) of the UAV image and Band 6 (740 nm) of a coincident Sentinel-2 image were not sensitive to an armyworm infestation in this study. Conclusions: We developed an accurate algorithm for the automated identification of armyworm-damaged corn plants using UAV images at the field scale. The study also indicated the feasibility of the developed method for monitoring corn armyworm damage at regional scale when combined with Sentinel-2 images.

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