Automatic Detection of Scattered Garbage Regions Using Small Unmanned Aerial Vehicle Low-Altitude Remote Sensing Images for High-Altitude Natural Reserve Environmental Protection

2021 ◽  
Vol 55 (6) ◽  
pp. 3604-3611
Author(s):  
Weiyang Chen ◽  
Yiyang Zhao ◽  
Tengfei You ◽  
Haifeng Wang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Environmental Protection ◽  
Unmanned Aerial Vehicle ◽  
Automatic Detection ◽  
Remote Sensing Images ◽  
Natural Reserve ◽  
Aerial Vehicle ◽  
Low Altitude

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

Automatic registration of Unmanned Aerial Vehicle remote sensing images based on an improved SIFT algorithm

2016 ◽  
Author(s):  
Tianjie Lei ◽  
Lin Li ◽  
Guangyuan Kan ◽  
Zhongbo Zhang ◽  
Tao Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Sift Algorithm ◽  
Aerial Vehicle

scholarly journals 3D target localization based on multi–unmanned aerial vehicle cooperation

2020 ◽  
pp. 002029402092226
Author(s):  
Cheng Xu ◽  
Chanjuan Yin ◽  
Daqing Huang ◽  
Wei Han ◽  
Dongzhen Wang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Target Localization ◽  
Weight Matrix ◽  
Target Point ◽  
Observation Data ◽  
Remote Sensing Images ◽  
Localization Method ◽  
Ground Target ◽  
Aerial Vehicle

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

scholarly journals Advantage and Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Survey

2020 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Qing Yan
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Good Imaging ◽  
Collection Data ◽  
Aerial Vehicle ◽  
Good Measurement ◽  
Engineering Survey ◽  
Low Altitude ◽  
High Work ◽  
Measurement Area

<p>Unmanned aerial vehicle remote sensing can fly at low altitude, shoot high-pixel, good-imaging images, and quickly acquire the geographic characteristics of the measurement area. It has the advantages of high periodic service quality, high work efficiency, wide monitoring range and good monitoring effect when applied in engineering survey. Unmanned aerial vehicle remote sensing has played a great role in the field of engineering survey, which can achieve data collection, data analysis and other work. At the same time, it can also obtain geographic information of the survey area in harsh environment, which has fast measurement speed and good measurement accuracy, and has very good application and development value.</p>

scholarly journals A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1221
Author(s):  
Haidong Zhang ◽  
Lingqing Wang ◽  
Ting Tian ◽  
Jianghai Yin
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Current Application ◽  
Rapid Acquisition ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Future Work

Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.

scholarly journals Preliminary analysis of the forest health state based on multispectral images acquired by Unmanned Aerial Vehicle

2015 ◽  
Vol 57 (3) ◽  
pp. 138-144 ◽  
Author(s):  
Paweł Czapski ◽  
Mariusz Kacprzak ◽  
Jan Kotlarz ◽  
Karol Mrowiec ◽  
Katarzyna Kubiak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Forest Health ◽  
Growing Season ◽  
Landsat 8 ◽  
Health State ◽  
Multispectral Images ◽  
Landsat 8 Oli ◽  
Aerial Vehicle ◽  
Low Altitude

Abstract The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV) that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m) is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager).

scholarly journals A NEW METHOD OF FAST REGISTRATION OF UNMANNED AERIAL VEHICLE REMOTE SENSING IMAGES BASED-ON AN IMPROVED SURF ALGORITHM

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 471-478
Author(s):  
T. J. Lei ◽  
R. R. Xu ◽  
J. H. Cheng ◽  
W. L. Song ◽  
W. Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Image ◽  
New Method ◽  
Processing Unit ◽  
Remote Sensing Images ◽  
Accuracy Rate ◽  
Matching Accuracy ◽  
Aerial Vehicle

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

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