Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network
Dongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of the Dongting Lake ecological wetland is of great + significance for the dynamic monitoring, protection, and sustainable utilization of the wetland. Therefore, this article proposes the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fevo.2022.944298/full |
| _version_ | 1811311927346855936 |
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| author | Diandi Wan Shaohua Yin |
| author_facet | Diandi Wan Shaohua Yin |
| author_sort | Diandi Wan |
| collection | DOAJ |
| description | Dongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of the Dongting Lake ecological wetland is of great + significance for the dynamic monitoring, protection, and sustainable utilization of the wetland. Therefore, this article proposes the information extraction of the Dongting Lake ecological wetland based on genetic algorithm optimized convolutional neural network (GA-CNN), an analysis model combining genetic algorithm (GA) and convolutional neural network (CNN). Firstly, we know the environmental information of Dongting Lake, take Gaofen-1 image as the data source, and use normalized vegetation index and normalized water body index as auxiliary data to preprocess the change detection of remote sensing images to obtain high-precision fitting images. GA-CNN is constructed to efficiently extract the information of the Dongting Lake ecological wetland, and the Relu excitation function is used to improve the phenomenon of gradient disappearance and convergence fluctuation so as to reduce the operation time. Logistic regression is used for feature extraction, and finally the automatic identification and information extraction of the Dongting Lake ecological wetland are realized. The research results show that the method proposed in this article can more deeply dig the information of ground objects, express depth features, and has high accuracy and credibility. |
| first_indexed | 2024-04-13T10:27:31Z |
| format | Article |
| id | doaj.art-8aa909a181324bf4a3aae07db35b77b6 |
| institution | Directory Open Access Journal |
| issn | 2296-701X |
| language | English |
| last_indexed | 2024-04-13T10:27:31Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ecology and Evolution |
| spelling | doaj.art-8aa909a181324bf4a3aae07db35b77b62022-12-22T02:50:17ZengFrontiers Media S.A.Frontiers in Ecology and Evolution2296-701X2022-07-011010.3389/fevo.2022.944298944298Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural NetworkDiandi WanShaohua YinDongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of the Dongting Lake ecological wetland is of great + significance for the dynamic monitoring, protection, and sustainable utilization of the wetland. Therefore, this article proposes the information extraction of the Dongting Lake ecological wetland based on genetic algorithm optimized convolutional neural network (GA-CNN), an analysis model combining genetic algorithm (GA) and convolutional neural network (CNN). Firstly, we know the environmental information of Dongting Lake, take Gaofen-1 image as the data source, and use normalized vegetation index and normalized water body index as auxiliary data to preprocess the change detection of remote sensing images to obtain high-precision fitting images. GA-CNN is constructed to efficiently extract the information of the Dongting Lake ecological wetland, and the Relu excitation function is used to improve the phenomenon of gradient disappearance and convergence fluctuation so as to reduce the operation time. Logistic regression is used for feature extraction, and finally the automatic identification and information extraction of the Dongting Lake ecological wetland are realized. The research results show that the method proposed in this article can more deeply dig the information of ground objects, express depth features, and has high accuracy and credibility.https://www.frontiersin.org/articles/10.3389/fevo.2022.944298/fullDongting Lakenormalized water bodywetland information extractionGA-CNNnormalized vegetation |
| spellingShingle | Diandi Wan Shaohua Yin Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network Frontiers in Ecology and Evolution Dongting Lake normalized water body wetland information extraction GA-CNN normalized vegetation |
| title | Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network |
| title_full | Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network |
| title_fullStr | Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network |
| title_full_unstemmed | Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network |
| title_short | Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network |
| title_sort | research on information extraction of the dongting lake ecological wetland based on genetic algorithm optimized convolutional neural network |
| topic | Dongting Lake normalized water body wetland information extraction GA-CNN normalized vegetation |
| url | https://www.frontiersin.org/articles/10.3389/fevo.2022.944298/full |
| work_keys_str_mv | AT diandiwan researchoninformationextractionofthedongtinglakeecologicalwetlandbasedongeneticalgorithmoptimizedconvolutionalneuralnetwork AT shaohuayin researchoninformationextractionofthedongtinglakeecologicalwetlandbasedongeneticalgorithmoptimizedconvolutionalneuralnetwork |