- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - A Method for Deriving Plant Temperature from UAV TIR Image
摘要： Crops are greatly affected by the temperature of farmland surface during their growing period. It is feasible to investigate the growth status of crops based on temperature information. For serving the research of crop growth status, the component temperature (e.g. temperature of vegetation and temperature of soil) are in need to be obtained. In this study, an unmanned aerial vehicle (UAV) temperature measurement system with a thermal infrared (TIR) imager and a charge-coupled device (CCD) camera is assembled and applied the brightness temperatures of farmland surface. The target areas were photographed by the UAV temperature measurement system according to a pre-set route, and obtain TIR and visible images. The component temperatures are obtained from the TIR image as following processes: (1) When shaded components are negligible at noon, two components, i.e. vegetation and soil, are divided by the OTSU algorithm; and (2) When shaded components cannot be ignored in the morning and afternoon, various components, i.e. vegetation, soil and concrete, the TIR image is divided into soil, vegetation and concrete by the corresponding classified visible images; Then, each of the components is divided into light and shaded components by the OTSU algorithm; thus, four components are obtained, including sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil. The derived component temperatures can serve as inputs to agricultural and water resource models.
关键词： farmland,TIR,UAV,surface temperature of crops
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Research on Empirical Model and Gap Rate Model for Estimating Rice Leaf Area Index Based on UAV HD Digital Images
摘要： The Leaf Area Index (LAI), as an important plant characteristic parameter, is of great significance for the monitoring of vegetation growth and the estimation of surface vegetation productivity. Rice is one of the world's major food crops, timely and accurate measurement of rice LAI can provide scientific information on agriculture. The remote sensing system of UAV is characterized by its cost-effective and real-time data acquisition. The method of estimating LAI by remote sensing technology has great advantages over traditional methods and has gradually become a frontier method for agricultural research. At present, the commonly used LAI inversion methods are empirical model method and physical model method. The former is not accurate because not all of the spectral information is used. The latter cannot directly calculate the analytical solution because of the complicated model and many input parameters. The main purpose of this paper is to obtain high-definition digital images of different varieties of rice using low-altitude drones, and to analyze the feasibility of estimating the LAI of rice canopy by empirical model method and porosity model method, and analyze the difference and estimation process between them. There are problems.
关键词： Leaf Area Index(LAI),Remote sensing,Empirical model,Unmanned Aerial Vehicle(UAV),Rice,Gap rate model
更新于2025-09-11 14:15:04
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Tracking Unmanned Aerial Vehicle CTU FTS - Application of equipment
摘要： Article which is about the Tracking Unmanned Aerial Vehicle continues in the description of the project development dealing with the utilization of the UAV (unmanned aerial vehicle). Documentation of the project progresses builds on the previous article. In that article the selection of observation and transmission equipment was summarized. In the article, the reader learns about an installation of the equipment on the UAV (helicopter), about an interconnection of the equipment to create complete and functional system, about testing of the UAV, about the solutions of the problems which came into being during the equipment against testing and about protection of unfavourable effects. The unmanned vehicle was chosen after a considering of several the parameters. These parameters are preservation of functionality or an influence to the balance. To find out how the added equipment affect the centre of gravity of the UAV the tabular method of the centre of gravity calculation was used. The results of the existing work on the project are location and attaching of the equipment to the unmanned vehicle, balance of the unmanned vehicle, solutions of the problems coming into being during the testing and design of the equipment protection against unfavourable effects.
关键词： Tracking Unmanned Aerial Vehicle CTU FTS,application of equipment,UAV,observation and transmission equipment,RPAS
更新于2025-09-11 14:15:04
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Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data
摘要： Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.
关键词： Lake Erie,UAV,HSI2,Pleiades,Remote sensing,Vegetation classification,Hyperspectral imaging
更新于2025-09-11 14:12:44
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The Improved Image Scrambling Algorithm for the Wireless Image Transmission Systems of UAVs
摘要： With the deepening of modern military reforms, information has become the key to winning modern warfare. The use of unmanned aerial vehicle (UAV) to capture image information has become an important means of reconnaissance in modern warfare and plays an irreplaceable role. The image information usually uses a wireless image transmission system, since image information is intercepted or stolen easily during the information transmission, encrypting an image is a common method for ensuring image security. However, traditional encryption algorithms have some deficiencies in terms of efficiency and security. In order to overcome these shortcomings, a new algorithm is proposed in this paper-an improved image scrambling encryption algorithm based on Fibonacci-p coding. The first new idea of the algorithm is to separate the positive and negative signs and data of the scrambled DCT coefficients, then form the symbol matrix and the data matrix respectively, perform the scrambling encryption operation on the symbol matrix. The second new idea is to encrypt the color RGB image by converting the R, G, and B colors into Y, Cb, and Cr, and converting the normal image operation into operations on Y, Cb, and Cr, thereby implementing the encryption operation. The comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm.
关键词： improved image scrambling encryption algorithm,Fibonacci-p coding,high-definition long-distance wireless digital image transmission system,UAV
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Palermo, Italy (2018.6.12-2018.6.15)] 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Assessment of PV Plant Monitoring System by Means of Unmanned Aerial Vehicles
摘要： Photovoltaic (PV) plant monitoring is recently representing an important field in the energy market, due to the large diffusion of PV plants, often built with low technical standards in the past decade and lack of accuracy in O&M activities after the first 5-year life of the plant. In this paper, the authors proposed an expert system that, based on the image integration obtained by means of Unmanned Aerial Vehicles (UAVs), is able to identify some defects in PV modules and to analyze from an economical point the impact of these defects.
关键词： economic analysis,PV monitoring,UAV
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Vignetting Correction of Post-Earthquake UAV Images
摘要： Because of bad weather conditions after destructive earthquake, UAV images are captured with serious vignetting phenomenon, which can significantly affects the speed and efficiency of image mosaic, especially the extraction of geological structure information and also the accuracy of post-earthquake quantitative damage extraction. An improved radial gradient correction method (IRGCM) was developed to correct the UAV images sequence, which were obtained in Lushan County after Ms7.0 Lushan, Sichuan, occurred on April 20, 2013. The results show that the corrected images have better radial homogeneity and clearer details, to a certain extent, which reduces the difficulties of image mosaic, and provides a better result for further analysis and damage information extraction. Also, the IRGCM is more robust and faster for massive UAV images vignetting removal.
关键词： UAV images,radial gradient,vignetting correction
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - UAV-BASED INTEGRATED MULTISENSOR PAYLOAD FOR HIGH RESOLUTION IMAGING
摘要： This paper describes the development of a multisensor UAV-based imaging platform. A sensor package consisting of a LiDAR, imaging spectrometer, and RGB camera were integrated with an inertial navigation system. This package is currently being flown on a hexacopter. A number of data acquisition missions have been conducted with this platform, including the imaging of a small active rotational mass movement, a wetland estuary, and several vineyards.
关键词： SfM point cloud,UAV,imaging spectrometer,LiDAR,INS
更新于2025-09-10 09:29:36
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Comparison of pixel-based and object-based image classification techniques in extracting information from UAV imagery data
摘要： As the rapid development is being focused in the urban area, there is a need for the utilisation of a rapid system for updating this profile immediately. One of the current technologies being applied in recent years is the use of unmanned aerial vehicle (UAV) for mapping purposes. The use of UAV is widespread in various fields because it is low cost, has high resolution and is able to fly at low altitude without the constraints of cloudy weather. Typically, the method of data extraction for UAV in Malaysia is still very limited and the traditional methods are still being implemented by some industries. The features from aerial photo orthomosaic are manually detected and digitised from visual interpretation for the mapping purposes. Unfortunately, these methods are tedious, expensive, consume much time, and may involve much fieldwork, to acquire only a limited information. Pixel-based technique is often used to extract low level features where the image is classified according to the spectral information where the pixels in the overlapping region will be misclassified due to the confusion among image analysis (OBIA) classification technique is widely used nowadays for automatic data extraction. Therefore, the general objective of this study is to assess the capability of UAV with high resolution data for image classifications. The pixel-based and OBIA classifications were compared using the Support Vector Machine (SVM) classifier. The classifications were assessed using different numbers of sample size. The result shows that OBIA gives a better result of Overall Accuracy (OA) than pixel-based. The consequences of this study accommodate further understanding and additional insight of utilising OBIA technique with different classifiers for the extended study.
关键词： object-based,UAV,SVM,pixel-based,image classification
更新于2025-09-10 09:29:36
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Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery
摘要： The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.
关键词： hyperspectral,spectroscopy,West Africa,tropical forest,UAV,Ghana,leaf traits,PLSR,leaf economic spectrum
更新于2025-09-10 09:29:36