1.3k citations · 2.8k across the 12 of their papers we have counts for
12 papers
AeroReformer2: Spoken-Query Referring Segmentation for Aerial Images
Rui Li, Chenxi Duan, Haoyang Yang
Spoken language offers a natural, hands-free interface for specifying an arbitrary target in dense remote-sensing imagery, yet existing referring remote-sensing image segmentation…
Building extraction with vision transformer
Libo Wang, Shenghui Fang, Rui Li +1
As an important carrier of human productive activities, the extraction of buildings is not only essential for urban dynamic monitoring but also necessary for suburban construction…
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…
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
Libo Wang, Rui Li, Dongzhi Wang +3
Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classifi…
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…
Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images
Libo Wang, Ce Zhang, Rui Li +3
Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with rapid development in sensor technologies…
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-…
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal
Chenxi Duan, Rui Li
In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of app…
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
Chenxi Duan, Jun Pan, Rui Li
In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the sce…
Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation
Rui Li, Jianlin Su, Chenxi Duan +1
In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs. The e…
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…
MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
Rui Li, Chenxi Duan, Shunyi Zheng +2
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder archit…