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 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 Estimating and evaluating the rice cluster distribution uniformity with UAV-based images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaohui Wang ◽  
Qiyuan Tang ◽  
Zhaozhong Chen ◽  
Youyi Luo ◽  
Hongyu Fu ◽  
...  
Keyword(s):  
Aerial Images ◽  
Whole Process ◽  
Location Determination ◽  
Cluster Distribution ◽  
Cluster Recognition ◽  
Aerial Vehicle ◽  
Distribution Uniformity ◽  
Cluster Location ◽  
Uav Images ◽  
Labeling Method

AbstractThe uniformity of the rice cluster distribution in the field affects population quality and the precise management of pesticides and fertilizers. However, there is no appropriate technical system for estimating and evaluating the uniformity at present. For that reason, a method based on unmanned aerial vehicle (UAV images) is proposed to estimate and evaluate the uniformity in this present study. This method includes rice cluster recognition and location determination based on the RGB color characteristics of the seedlings of aerial images, region segmentation considering the rice clusters based on Voronoi Diagram, and uniformity index definition for evaluating the rice cluster distribution based on the variation coefficient. The results indicate the rice cluster recognition attains a high precision, with the precision, accuracy, recall, and F1-score of rice cluster recognition reaching > 95%, 97%, 97%, 95%, and 96%, respectively. The rice cluster location error is small and obeys the gamma (3.00, 0.54) distribution (mean error, 1.62 cm). The uniformity index is reasonable for evaluating the rice cluster distribution verified via simulation. As a whole process, the estimating method is sufficiently high accuracy with relative error less than 0.01% over the manual labeling method. Therefore, this method based on UAV images is feasible, convenient, technologically advanced, inexpensive, and highly precision for the estimation and evaluation of the rice cluster distribution uniformity. However, the evaluation application indicates that there is much room for improvement in terms of the uniformity of mechanized paddy field transplanting in South China.

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 Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Drones ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 39 ◽  
Author(s):  
Ovidiu Csillik ◽  
John Cherbini ◽  
Robert Johnson ◽  
Andy Lyons ◽  
Maggi Kelly
Keyword(s):  
Precision Agriculture ◽  
Fruit Production ◽  
Research Priority ◽  
Disease Occurrence ◽  
Aerial Vehicle ◽  
Simple Linear Iterative Clustering ◽  
Uav Images ◽  
Crop Condition ◽  
Individual Trees ◽  
Citrus Trees

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

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).

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 SEMANTIC SEGMENTATION AND UNREGISTERED BUILDING DETECTION FROM UAV IMAGES USING A DECONVOLUTIONAL NETWORK

2018 ◽  
Vol XLII-2 ◽  
pp. 419-424
Author(s):  
S. Ham ◽  
Y. Oh ◽  
K. Choi ◽  
I. Lee
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Semantic Segmentation ◽  
Geospatial Data ◽  
Aerial Images ◽  
Building Detection ◽  
Urban Management ◽  
Probability Map ◽  
Gis Data ◽  
Uav Images ◽  
Uav Image

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

scholarly journals Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

2019 ◽  
Vol 11 (24) ◽  
pp. 3012 ◽  
Author(s):  
Licheng Zhao ◽  
Yun Shi ◽  
Bin Liu ◽  
Ciara Hovis ◽  
Yulin Duan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Optimal Choice ◽  
Fragmented Landscape ◽  
Agricultural Insurance ◽  
Cropping Patterns ◽  
Crop Mapping ◽  
Sentinel 2A ◽  
Uav Images ◽  
Uav Image

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

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.

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