Crop classification of multitemporal PolSAR based on 3-D attention module with ViT
Multitemporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multitemporal polarimetric SAR data classification methods, the simultaneous temporal–polarimetric–spatial f...
| Autores: | , , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/390748 |
| Acesso em linha: | https://hdl.handle.net/2117/390748 https://dx.doi.org/10.1109/LGRS.2023.3270488 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Synthetic aperture radar Polarimetric remote sensing Imaging systems in geophysics Crop classification Multitemporal PolSAR Temporal–polarimetric–spatial characteristics Vision transformer (ViT) Radar d'obertura sintètica Imatgeria en geofísica Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar |
| Resumo: | Multitemporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multitemporal polarimetric SAR data classification methods, the simultaneous temporal–polarimetric–spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this letter, we propose a classification model that combines a dual-stream network as a temporal–polarimetric–spatial feature extraction module with vision transformer (ViT) called temporal–polarimetric–spatial transformer (TPST) to address the above problems. Second, a 3-D convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both the UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet. |
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