SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing
In the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8959217/ |
| _version_ | 1818646441845850112 |
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| author | Chunxue Wu Bobo Ju Yan Wu Naixue Xiong |
| author_facet | Chunxue Wu Bobo Ju Yan Wu Naixue Xiong |
| author_sort | Chunxue Wu |
| collection | DOAJ |
| description | In the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UAV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UAV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages. |
| first_indexed | 2024-12-17T00:46:42Z |
| format | Article |
| id | doaj.art-e02b42e536154cc9b2aab658665db182 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:46:42Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e02b42e536154cc9b2aab658665db1822022-12-21T22:09:54ZengIEEEIEEE Access2169-35362020-01-018151441515810.1109/ACCESS.2020.29664978959217SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote SensingChunxue Wu0https://orcid.org/0000-0001-8498-3881Bobo Ju1https://orcid.org/0000-0001-7769-5646Yan Wu2Naixue Xiong3School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaO’Neill School of Public and Environmental Affairs, Indiana University Bloomington, Bloomington, IN, USASchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaIn the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UAV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UAV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages.https://ieeexplore.ieee.org/document/8959217/SlimRGBDResNetgenerative adversarial networksimage noise reductionUAVchannel pruning |
| spellingShingle | Chunxue Wu Bobo Ju Yan Wu Naixue Xiong SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing IEEE Access SlimRGBD ResNet generative adversarial networks image noise reduction UAV channel pruning |
| title | SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing |
| title_full | SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing |
| title_fullStr | SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing |
| title_full_unstemmed | SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing |
| title_short | SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing |
| title_sort | slimrgbd a geographic information photography noise reduction system for aerial remote sensing |
| topic | SlimRGBD ResNet generative adversarial networks image noise reduction UAV channel pruning |
| url | https://ieeexplore.ieee.org/document/8959217/ |
| work_keys_str_mv | AT chunxuewu slimrgbdageographicinformationphotographynoisereductionsystemforaerialremotesensing AT boboju slimrgbdageographicinformationphotographynoisereductionsystemforaerialremotesensing AT yanwu slimrgbdageographicinformationphotographynoisereductionsystemforaerialremotesensing AT naixuexiong slimrgbdageographicinformationphotographynoisereductionsystemforaerialremotesensing |