Deep feature extraction and motion representation for satellite video scene classification
Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gu, Yanfeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Heidelberg : Springer, 2001, 63(2020), 4 vom: 09. März |
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| Übergeordnetes Werk: |
volume:63 ; year:2020 ; number:4 ; day:09 ; month:03 |
| Links: |
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| DOI / URN: |
10.1007/s11432-019-2784-4 |
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SPR039120929 |
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| 520 | |a Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. | ||
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10.1007/s11432-019-2784-4 doi (DE-627)SPR039120929 (SPR)s11432-019-2784-4-e DE-627 ger DE-627 rakwb eng Gu, Yanfeng verfasserin aut Deep feature extraction and motion representation for satellite video scene classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. satellite videos (dpeaa)DE-He213 classification (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 CNN (dpeaa)DE-He213 long and short term memory (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 motion representation (dpeaa)DE-He213 Liu, Huan aut Wang, Tengfei aut Li, Shengyang aut Gao, Guoming aut Enthalten in Science in China Heidelberg : Springer, 2001 63(2020), 4 vom: 09. März (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:63 year:2020 number:4 day:09 month:03 https://dx.doi.org/10.1007/s11432-019-2784-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 63 2020 4 09 03 |
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10.1007/s11432-019-2784-4 doi (DE-627)SPR039120929 (SPR)s11432-019-2784-4-e DE-627 ger DE-627 rakwb eng Gu, Yanfeng verfasserin aut Deep feature extraction and motion representation for satellite video scene classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. satellite videos (dpeaa)DE-He213 classification (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 CNN (dpeaa)DE-He213 long and short term memory (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 motion representation (dpeaa)DE-He213 Liu, Huan aut Wang, Tengfei aut Li, Shengyang aut Gao, Guoming aut Enthalten in Science in China Heidelberg : Springer, 2001 63(2020), 4 vom: 09. März (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:63 year:2020 number:4 day:09 month:03 https://dx.doi.org/10.1007/s11432-019-2784-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 63 2020 4 09 03 |
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10.1007/s11432-019-2784-4 doi (DE-627)SPR039120929 (SPR)s11432-019-2784-4-e DE-627 ger DE-627 rakwb eng Gu, Yanfeng verfasserin aut Deep feature extraction and motion representation for satellite video scene classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. satellite videos (dpeaa)DE-He213 classification (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 CNN (dpeaa)DE-He213 long and short term memory (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 motion representation (dpeaa)DE-He213 Liu, Huan aut Wang, Tengfei aut Li, Shengyang aut Gao, Guoming aut Enthalten in Science in China Heidelberg : Springer, 2001 63(2020), 4 vom: 09. März (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:63 year:2020 number:4 day:09 month:03 https://dx.doi.org/10.1007/s11432-019-2784-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 63 2020 4 09 03 |
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10.1007/s11432-019-2784-4 doi (DE-627)SPR039120929 (SPR)s11432-019-2784-4-e DE-627 ger DE-627 rakwb eng Gu, Yanfeng verfasserin aut Deep feature extraction and motion representation for satellite video scene classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. satellite videos (dpeaa)DE-He213 classification (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 CNN (dpeaa)DE-He213 long and short term memory (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 motion representation (dpeaa)DE-He213 Liu, Huan aut Wang, Tengfei aut Li, Shengyang aut Gao, Guoming aut Enthalten in Science in China Heidelberg : Springer, 2001 63(2020), 4 vom: 09. März (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:63 year:2020 number:4 day:09 month:03 https://dx.doi.org/10.1007/s11432-019-2784-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 63 2020 4 09 03 |
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10.1007/s11432-019-2784-4 doi (DE-627)SPR039120929 (SPR)s11432-019-2784-4-e DE-627 ger DE-627 rakwb eng Gu, Yanfeng verfasserin aut Deep feature extraction and motion representation for satellite video scene classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. satellite videos (dpeaa)DE-He213 classification (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 CNN (dpeaa)DE-He213 long and short term memory (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 motion representation (dpeaa)DE-He213 Liu, Huan aut Wang, Tengfei aut Li, Shengyang aut Gao, Guoming aut Enthalten in Science in China Heidelberg : Springer, 2001 63(2020), 4 vom: 09. März (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:63 year:2020 number:4 day:09 month:03 https://dx.doi.org/10.1007/s11432-019-2784-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 63 2020 4 09 03 |
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Deep feature extraction and motion representation for satellite video scene classification |
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Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Abstract Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. 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