Unmanned Aerial Vehicle remote sensing image dehazing via global parameters

2020 ◽  
Author(s):  
Yufeng Fan ◽  
Yongfeng Cao ◽  
Xiuzhang Yang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Image ◽  
Image Dehazing ◽  
Aerial Vehicle ◽  
Global Parameters

scholarly journals An unmanned aerial vehicle visible light remote sensing image dataset of four forest dynamic plots in subtropical China

2021 ◽  
Vol 29 (9) ◽  
pp. 1181-1185
Author(s):  
Zhaochen Zhang ◽  
Jianbo Hu ◽  
Qingsong Yang ◽  
Juyu Lian ◽  
Buhang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Visible Light ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Image ◽  
Subtropical China ◽  
Forest Dynamic ◽  
Image Dataset ◽  
Aerial Vehicle

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.

scholarly journals Ghost Elimination via Multi-Component Collaboration for Unmanned Aerial Vehicle Remote Sensing Image Stitching

2021 ◽  
Vol 13 (7) ◽  
pp. 1388
Author(s):  
Wanli Xue ◽  
Zhe Zhang ◽  
Shengyong Chen
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Moving Objects ◽  
Information Source ◽  
Remote Sensing Image ◽  
Selection Strategy ◽  
Image Stitching ◽  
Data Set ◽  
Image Information ◽  
Aerial Vehicle

Ghosts are a common phenomenon widely present in unmanned aerial vehicle (UAV) remote sensing image stitching that seriously affect the naturalness of stitching results. In order to effectively remove ghosts and produce visually natural stitching results, we propose a novel image stitching method that can identify and eliminate ghosts through multi-component collaboration without object distortion, segmentation or repetition. Specifically, our main contributions are as follows: first, we propose a ghost identification component to locate a potential ghost in the stitching area; and detect significantly moving objects in the two stitched images. In particular, due to the characteristics of UAV shooting, the objects in UAV remote sensing images are small and the image quality is poor. We propose a mesh-based image difference comparison method to identify ghosts; and use an object tracking algorithm to accurately correspond to each ghost pair. Second, we design an image information source selection strategy to generate the ghost replacement region, which can replace the located ghost and avoid object distortion, segmentation and repetition. Third, we find that the process of ghost elimination can produce natural mosaic images by eliminating the ghost caused by initial blending with selected image information source. We validate the proposed method on VIVID data set and compare our method with Homo, ELA, SPW and APAP using the peak signal to noise ratio (PSNR) evaluation indicator.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

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.

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