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- Lancaster UniversityGB10 papers
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6 papers · 1 filter
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
Libo Wang, Rui Li, Ce Zhang +4
Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications, such as land cover mapping, urban change detection, environmental…
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
Libo Wang, Rui Li, Chenxi Duan +3
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an en…
ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
Rui Li, Chenxi Duan
Semantic segmentation of remotely sensed images plays a crucial role in precision agriculture, environmental protection, and economic assessment. In recent years, substantial fine-…
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
Rui Li, Shunyi Zheng, Ce Zhang +2
Semantic segmentation using fine-resolution remotely sensed images plays a critical role in many practical applications, such as urban planning, environmental protection, natural a…
Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
Rui Li, Shunyi Zheng, Chenxi Duan +2
The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer visi…
Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network
Rui Li, Shunyi Zheng, Chenxi Duan +1
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2…