MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens
Deep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data. To alleviate this problem, this paper proposes a PolSAR image classification method with a Masked Autoen...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4280 |
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| author | Jianlong Wang Yingying Li Dou Quan Beibei Hou Zhensong Wang Haifeng Sima Junding Sun |
| author_facet | Jianlong Wang Yingying Li Dou Quan Beibei Hou Zhensong Wang Haifeng Sima Junding Sun |
| author_sort | Jianlong Wang |
| collection | DOAJ |
| description | Deep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data. To alleviate this problem, this paper proposes a PolSAR image classification method with a Masked Autoencoder based on Position prediction and Memory tokens (MAPM). First, MAPM designs a Masked Autoencoder (MAE) based on the transformer for pre-training, which can boost feature learning and improve classification results based on the number of labeled samples. Secondly, since the transformer is relatively insensitive to the order of the input tokens, a position prediction strategy is introduced in the encoder part of the MAE. It can effectively capture subtle differences and discriminate complex, blurry boundaries in PolSAR images. In the fine-tuning stage, the addition of learnable memory tokens can improve classification performance. In addition, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="italic">L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss is used for MAE optimization to enhance the robustness of the model to outliers in PolSAR data. Experimental results show the effectiveness and advantages of the proposed MAPM in PolSAR image classification. Specifically, MAPM achieves performance gains of about 1% in classification accuracy compared with existing methods. |
| format | Article |
| id | doaj-art-15811c15a45a400cbbd97d17e6cbe2de |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-15811c15a45a400cbbd97d17e6cbe2de2025-08-20T02:27:38ZengMDPI AGRemote Sensing2072-42922024-11-011622428010.3390/rs16224280MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory TokensJianlong Wang0Yingying Li1Dou Quan2Beibei Hou3Zhensong Wang4Haifeng Sima5Junding Sun6School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Software, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaDeep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data. To alleviate this problem, this paper proposes a PolSAR image classification method with a Masked Autoencoder based on Position prediction and Memory tokens (MAPM). First, MAPM designs a Masked Autoencoder (MAE) based on the transformer for pre-training, which can boost feature learning and improve classification results based on the number of labeled samples. Secondly, since the transformer is relatively insensitive to the order of the input tokens, a position prediction strategy is introduced in the encoder part of the MAE. It can effectively capture subtle differences and discriminate complex, blurry boundaries in PolSAR images. In the fine-tuning stage, the addition of learnable memory tokens can improve classification performance. In addition, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="italic">L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss is used for MAE optimization to enhance the robustness of the model to outliers in PolSAR data. Experimental results show the effectiveness and advantages of the proposed MAPM in PolSAR image classification. Specifically, MAPM achieves performance gains of about 1% in classification accuracy compared with existing methods.https://www.mdpi.com/2072-4292/16/22/4280polarimetric SARmasked autoencoderposition prediction<i>L</i><sub>1</sub> lossmemory tokens |
| spellingShingle | Jianlong Wang Yingying Li Dou Quan Beibei Hou Zhensong Wang Haifeng Sima Junding Sun MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens Remote Sensing polarimetric SAR masked autoencoder position prediction <i>L</i><sub>1</sub> loss memory tokens |
| title | MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens |
| title_full | MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens |
| title_fullStr | MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens |
| title_full_unstemmed | MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens |
| title_short | MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens |
| title_sort | mapm polsar image classification with masked autoencoder based on position prediction and memory tokens |
| topic | polarimetric SAR masked autoencoder position prediction <i>L</i><sub>1</sub> loss memory tokens |
| url | https://www.mdpi.com/2072-4292/16/22/4280 |
| work_keys_str_mv | AT jianlongwang mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT yingyingli mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT douquan mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT beibeihou mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT zhensongwang mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT haifengsima mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens AT jundingsun mapmpolsarimageclassificationwithmaskedautoencoderbasedonpositionpredictionandmemorytokens |