Deep Learning for Automatic Recognition of Oil Production Related Objects based on High-Resolution Remote Sensing Imagery

2020 ◽  
Author(s):  
Nannan Zhang ◽  
Hang Zhao ◽  
Yang Liu ◽  
Song Liu ◽  
Zhiguo Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Oil Production ◽  
Automatic Recognition ◽  
Remote Sensing Imagery

scholarly journals Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

2018 ◽  
Vol 10 (1) ◽  
pp. 144 ◽  
Author(s):  
Yongyang Xu ◽  
Liang Wu ◽  
Zhong Xie ◽  
Zhanlong Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Very High

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

Quadtree Decomposition-based Deep Learning Method for Multiscale Coastline Extraction with High-Resolution Remote Sensing Imagery

2021 ◽  
Author(s):  
Shuting Sun ◽  
Lin Mu ◽  
Ruyi Feng ◽  
Yifu Chen ◽  
Lizhe Wang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Method ◽  
Quadtree Decomposition ◽  
Remote Sensing Imagery

scholarly journals Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 585 ◽  
Author(s):  
Yang Chen ◽  
Rongshuang Fan ◽  
Xiucheng Yang ◽  
Jingxue Wang ◽  
Aamir Latif
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Water Bodies ◽  
Urban Water ◽  
Remote Sensing Imagery

scholarly journals Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model

2020 ◽  
Vol 12 (15) ◽  
pp. 2426
Author(s):  
Alin-Ionuț Pleșoianu ◽  
Mihai-Sorin Stupariu ◽  
Ionuț Șandric ◽  
Ileana Pătru-Stupariu ◽  
Lucian Drăguț
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Ensemble Model ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Ensemble Models ◽  
Remote Sensing Imagery ◽  
Very High

Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped.

Extraction of snow cover from high-resolution remote sensing imagery using deep learning on a small dataset

2019 ◽  
Vol 11 (1) ◽  
pp. 66-75
Author(s):  
Xuejun Guo ◽  
Yin Chen ◽  
Xiaofeng Liu ◽  
Yue Zhao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Snow Cover ◽  
Remote Sensing Imagery ◽  
Small Dataset

Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning

2020 ◽  
Vol 250 ◽  
pp. 112045 ◽  
Author(s):  
Yansheng Li ◽  
Wei Chen ◽  
Yongjun Zhang ◽  
Chao Tao ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Cloud Detection ◽  
Remote Sensing Imagery ◽  
Weakly Supervised
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