Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination
Spatial infrared spot target (SIST) discrimination based on infrared radiation sequences (IRSs) can be considered a univariate trending time series classification task. However, due to the complexity of actual scenarios and the limited opportunities for acquiring IRSs, resulting in noise interferenc...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4624 |
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| author | Huiying Liu Jiarong Wang Weijun Zhong Haitao Nie Xiaotong Deng Jiaqi Sun Ming Zhu Ming Wei |
| author_facet | Huiying Liu Jiarong Wang Weijun Zhong Haitao Nie Xiaotong Deng Jiaqi Sun Ming Zhu Ming Wei |
| author_sort | Huiying Liu |
| collection | DOAJ |
| description | Spatial infrared spot target (SIST) discrimination based on infrared radiation sequences (IRSs) can be considered a univariate trending time series classification task. However, due to the complexity of actual scenarios and the limited opportunities for acquiring IRSs, resulting in noise interference, extremely small-scale datasets with imbalanced distribution of classes and widely varying sequence lengths range from a few hundred to several thousand time steps. Current research is primarily based on idealized simulation datasets, resulting in a performance gap when applied to actual applications. To address these issues, firstly, we construct a simulation dataset tailored to the challenges of actual scenarios. Secondly, we design a practical data preprocessing method to achieve uniform sequence length, coarse alignment of shapelets and filtering while preserving key points. Thirdly, we propose a residual network Res-LK-SLR for IRS classification based on large kernels (LKs, providing long-term dependence) and shapelet-level representations (SLRs, where the hidden layer features are aligned with the learned high-level representations to obtain the optimal segmentation and generate shapelet-level representations). Additionally, we conduct extensive evaluations and validations on both the simulation dataset and 18 UCR time series classification datasets. The results demonstrate the effectiveness and generalization ability of our proposed Res-LK-SLR. |
| format | Article |
| id | doaj-art-3f68463c47d24ae98b1242d61a748a19 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3f68463c47d24ae98b1242d61a748a192024-12-27T14:50:43ZengMDPI AGRemote Sensing2072-42922024-12-011624462410.3390/rs16244624Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target DiscriminationHuiying Liu0Jiarong Wang1Weijun Zhong2Haitao Nie3Xiaotong Deng4Jiaqi Sun5Ming Zhu6Ming Wei7University of Chinese Academy of Sciences, Beijing 100049, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaSatellite Information Intelligent Processing and Application Research Laboratory, Beijing 100192, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaSatellite Information Intelligent Processing and Application Research Laboratory, Beijing 100192, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaSpatial infrared spot target (SIST) discrimination based on infrared radiation sequences (IRSs) can be considered a univariate trending time series classification task. However, due to the complexity of actual scenarios and the limited opportunities for acquiring IRSs, resulting in noise interference, extremely small-scale datasets with imbalanced distribution of classes and widely varying sequence lengths range from a few hundred to several thousand time steps. Current research is primarily based on idealized simulation datasets, resulting in a performance gap when applied to actual applications. To address these issues, firstly, we construct a simulation dataset tailored to the challenges of actual scenarios. Secondly, we design a practical data preprocessing method to achieve uniform sequence length, coarse alignment of shapelets and filtering while preserving key points. Thirdly, we propose a residual network Res-LK-SLR for IRS classification based on large kernels (LKs, providing long-term dependence) and shapelet-level representations (SLRs, where the hidden layer features are aligned with the learned high-level representations to obtain the optimal segmentation and generate shapelet-level representations). Additionally, we conduct extensive evaluations and validations on both the simulation dataset and 18 UCR time series classification datasets. The results demonstrate the effectiveness and generalization ability of our proposed Res-LK-SLR.https://www.mdpi.com/2072-4292/16/24/4624spatial infrared spot target (SIST) discriminationlarge kernels (LKs)shapelet-level representations (SLRs)oriented toward the real scenario |
| spellingShingle | Huiying Liu Jiarong Wang Weijun Zhong Haitao Nie Xiaotong Deng Jiaqi Sun Ming Zhu Ming Wei Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination Remote Sensing spatial infrared spot target (SIST) discrimination large kernels (LKs) shapelet-level representations (SLRs) oriented toward the real scenario |
| title | Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination |
| title_full | Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination |
| title_fullStr | Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination |
| title_full_unstemmed | Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination |
| title_short | Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination |
| title_sort | res lk slr a residual network based on large kernels and shapelet level representations for spatial infrared spot target discrimination |
| topic | spatial infrared spot target (SIST) discrimination large kernels (LKs) shapelet-level representations (SLRs) oriented toward the real scenario |
| url | https://www.mdpi.com/2072-4292/16/24/4624 |
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