scholarly journals An End-to-End System for Unmanned Aerial Vehicle High-Resolution Remote Sensing Image Haze Removal Algorithm Using Convolution Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158787-158797
Author(s):  
Yufeng Li ◽  
Jingbo Ren ◽  
Yufeng Huang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Haze Removal ◽  
Aerial Vehicle ◽  
End To End

High Resolution Remote Sensing Image Classification Combining with Mean-Shift Segmentation and Fully Convolution Neural Network

2018 ◽  
Vol 55 (2) ◽  
pp. 022802
Author(s):  
方旭 Fang Xu ◽  
王光辉 Wang Guanghui ◽  
杨化超 Yang Huachao ◽  
刘慧杰 Liu Huijie ◽  
闫立波 Yan Libo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Mean Shift ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Image Classification

scholarly journals Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation

2020 ◽  
Vol 12 (4) ◽  
pp. 625 ◽  
Author(s):  
Yantong Chen ◽  
Yuyang Li ◽  
Junsheng Wang ◽  
Weinan Chen ◽  
Xianzhong Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Random Field ◽  
Conditional Random Field ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Deep Convolution Neural Network ◽  
End To End

Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images.

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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