A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
نویسندگان
چکیده
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. utilizes encoder to capture multilevel feature maps, which are incorporated into final prediction by a decoder. As context is crucial precise segmentation, tremendous effort made extract such information in intelligent fashion, including employing dilated/atrous convolutions or inserting attention modules. However, these endeavors all based on FCN ResNet other backbones, cannot exploit from theoretical concept. By contrast, we introduce Swin Transformer as backbone and design novel decoder of densely connected aggregation module (DCFAM) restore resolution produce segmentation map. experimental results two remotely sensed datasets demonstrate effectiveness proposed scheme.
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ژورنال
عنوان ژورنال: IEEE Geoscience and Remote Sensing Letters
سال: 2022
ISSN: ['1558-0571', '1545-598X']
DOI: https://doi.org/10.1109/lgrs.2022.3143368