Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion.
The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a ...
Jan 18, 2024 · The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal ...
The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a ...
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Abstract: We propose a SAR and Optical Fusion Network based on the UNet framework (SOF-UNet) for multi-modal land cover classification.
Learning SAR-Optical Cross Modal Features for Land Cover Classification. Remote Sens. 2024, 16, 431. https://doi.org/10.3390/rs16020431. AMA Style. Quan Y ...
In this paper, a novel approach for selection of relevant features in SAR-ATR is proposed. The main concern of all studies in this filed is the accuracy. For ...
Jan 5, 2024 · In this paper, we introduce Fus-MAE, a self-supervised learning framework based on masked au- toencoders that uses cross-attention to perform ...
Synthetic Aperture Radar (SAR) images have proven to be a valuable cue for multimodal Land Cover Classification (LCC) when combined with RGB images.
We propose an innovative assisted learning framework that employs a “teacher-student” architecture equipped with local and global distillation schemes.