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Attention-Based Multiscale Sequential Network for PolSAR Image Classification
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DOI：10.1109/LGRS.2022.3164464.png)
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摘要
En 中文
Polarimetric synthetic aperture radar (PolSAR) images classification is an important topic for PolSAR images understanding and interpretation. However, traditional pixel-based PolSAR image classification that takes image pixel as a processing unit cannot make full use of spatial information and, thus, may not obtain the satisfactory classification results. Hence, this letter proposed an attention-based multiscale sequential network for PolSAR images classification increasing the multiscale spatial information between pixels by way of spatial sequence. Specifically, the long short-term memory (LSTM) network is introduced to convert the time sequence into spatial sequence to extract the spatial features. Then, to obtain the more abundant spatial features and select more important spatial information, an attention-based multiscale spatial-enhanced LSTM (AMSE-LSTM) is proposed to enhance the relationship between pixel spatial information. Finally, a new mixed loss function is defined to improve the classification performance. Experimental results with two real PolSAR data show that compared with state-of-the-art methods, the proposed method can achieve a much better performance and overall classification accuracy.
Keyword:
Feature extraction
Image classification
Spatial databases
Covariance matrices
Training
Logic gates
Deep learning
Attention mechanism
long short-term memory (LSTM)
polarimetric synthetic aperture radar (PolSAR) classification
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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