Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection
To meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly co...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/217 |
| _version_ | 1827617233585569792 |
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| author | Lirong Liu Xinming Tang Yuhang Gan Shucheng You Zhengyu Luo Lei Du Yun He |
| author_facet | Lirong Liu Xinming Tang Yuhang Gan Shucheng You Zhengyu Luo Lei Du Yun He |
| author_sort | Lirong Liu |
| collection | DOAJ |
| description | To meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly constructed bare land based on remote sensing images. A generalized deep convolutional neural network change detection model framework integrating multi-scale information was developed for the automatic extraction of change information. To resolve the problems in the automatic extraction of new bare land parcels, such as mis-extractions and parcel fragmentation, a proximity evaluation model that integrates the confidence-based semantic distance and spatial distance between parcels and their overlapping area is proposed to perform parcel aggregation. Additionally, we propose a complete set of optimized processing techniques from pixel pre-processing to vector post-processing. The results demonstrated that the aggregation method developed in this study is more targeted and effective than ArcGIS for the automatically extracted land change parcels. Additionally, compared with the initial parcels, the total number of optimized parcels decreased by more than 50% and the false detection rate decreased by approximately 30%. These results indicate that this method can markedly reduce the overall data volume and false detection rate of automatically extracted parcels through post-processing under certain conditions of the model and samples and provide technical support for applying the results of automatic feature extraction in engineering practices. |
| first_indexed | 2024-03-09T09:41:57Z |
| format | Article |
| id | doaj.art-b1d34db595e84b29a4f6b010d4efb5ce |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:41:57Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b1d34db595e84b29a4f6b010d4efb5ce2023-12-02T00:51:51ZengMDPI AGRemote Sensing2072-42922022-12-0115121710.3390/rs15010217Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change DetectionLirong Liu0Xinming Tang1Yuhang Gan2Shucheng You3Zhengyu Luo4Lei Du5Yun He6Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing100048, ChinaTo meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly constructed bare land based on remote sensing images. A generalized deep convolutional neural network change detection model framework integrating multi-scale information was developed for the automatic extraction of change information. To resolve the problems in the automatic extraction of new bare land parcels, such as mis-extractions and parcel fragmentation, a proximity evaluation model that integrates the confidence-based semantic distance and spatial distance between parcels and their overlapping area is proposed to perform parcel aggregation. Additionally, we propose a complete set of optimized processing techniques from pixel pre-processing to vector post-processing. The results demonstrated that the aggregation method developed in this study is more targeted and effective than ArcGIS for the automatically extracted land change parcels. Additionally, compared with the initial parcels, the total number of optimized parcels decreased by more than 50% and the false detection rate decreased by approximately 30%. These results indicate that this method can markedly reduce the overall data volume and false detection rate of automatically extracted parcels through post-processing under certain conditions of the model and samples and provide technical support for applying the results of automatic feature extraction in engineering practices.https://www.mdpi.com/2072-4292/15/1/217remote sensingchange detectionnew bare landdeep learningparcel processing |
| spellingShingle | Lirong Liu Xinming Tang Yuhang Gan Shucheng You Zhengyu Luo Lei Du Yun He Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection Remote Sensing remote sensing change detection new bare land deep learning parcel processing |
| title | Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection |
| title_full | Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection |
| title_fullStr | Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection |
| title_full_unstemmed | Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection |
| title_short | Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection |
| title_sort | research on optimization of processing parcels of new bare land based on remote sensing image change detection |
| topic | remote sensing change detection new bare land deep learning parcel processing |
| url | https://www.mdpi.com/2072-4292/15/1/217 |
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