Neural network big data fusion in remote sensing image processing technology
Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-03-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2023-0147 |
| _version_ | 1797258370576547840 |
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| author | Wu Xiaobo |
| author_facet | Wu Xiaobo |
| author_sort | Wu Xiaobo |
| collection | DOAJ |
| description | Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks. |
| first_indexed | 2024-04-24T22:52:27Z |
| format | Article |
| id | doaj.art-4ea9ab42ec4d40699e7b0291c2e90dec |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-24T22:52:27Z |
| publishDate | 2024-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-4ea9ab42ec4d40699e7b0291c2e90dec2024-03-18T10:27:51ZengDe GruyterJournal of Intelligent Systems2191-026X2024-03-0133144203410.1515/jisys-2023-0147Neural network big data fusion in remote sensing image processing technologyWu Xiaobo0School of Business, Lingnan Normal University, Zhanjiang, 524048, Guangdong, ChinaRemote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.https://doi.org/10.1515/jisys-2023-0147remote sensing imageimage processingneural networksbig datahigh dimensional data analysis |
| spellingShingle | Wu Xiaobo Neural network big data fusion in remote sensing image processing technology Journal of Intelligent Systems remote sensing image image processing neural networks big data high dimensional data analysis |
| title | Neural network big data fusion in remote sensing image processing technology |
| title_full | Neural network big data fusion in remote sensing image processing technology |
| title_fullStr | Neural network big data fusion in remote sensing image processing technology |
| title_full_unstemmed | Neural network big data fusion in remote sensing image processing technology |
| title_short | Neural network big data fusion in remote sensing image processing technology |
| title_sort | neural network big data fusion in remote sensing image processing technology |
| topic | remote sensing image image processing neural networks big data high dimensional data analysis |
| url | https://doi.org/10.1515/jisys-2023-0147 |
| work_keys_str_mv | AT wuxiaobo neuralnetworkbigdatafusioninremotesensingimageprocessingtechnology |