An Empirical Study of Remote Sensing Pretraining
نویسندگان
چکیده
Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with ImageNet pretrained weights since natural images inevitably present large domain gap relative to images, probably limiting fine-tuning performance on downstream scene tasks. This issue motivates us conduct an empirical study RS pretraining (RSP) images. To this end, we train different networks from scratch help largest recognition dataset up now—MillionAID—to obtain series backbones, including both convolutional neural (CNNs) vision transformers, such as Swin ViTAE, which have shown promising computer Then, investigate impact RSP representative tasks, recognition, semantic segmentation, object detection, change detection using these CNN transformer backbones. Empirical shows that can deliver distinctive performances in tasks perceiving RS-related semantics, “Bridge” “Airplane.” We also find that, although mitigates data discrepancies traditional it may still suffer task discrepancies, where require representations These findings call further efforts large-scale datasets effective methods. The codes will be released at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing .
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2023
ISSN: ['0196-2892', '1558-0644']
DOI: https://doi.org/10.1109/tgrs.2022.3176603