10 citations · 16 across the 7 of their papers we have counts for
10 papers
FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation
Xiaokang Zhang, Xuran Xiong, Jianzhong Huang +1
Remote sensing image segmentation (RSIS) in federated environments has gained increasing attention because it enables collaborative model training across distributed datasets witho…
SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
Xiaokang Zhang, Bo Li, Chufeng Zhou +2
Pretraining and fine-tuning have emerged as a new paradigm in remote sensing image interpretation. Among them, Masked Autoencoder (MAE)-based pretraining stands out for its strong…
Kolmogorov-Arnold Network for Remote Sensing Image Semantic Segmentation
Xianping Ma, Ziyao Wang, Yin Hu +2
Semantic segmentation plays a crucial role in remote sensing applications, where the accurate extraction and representation of features are essential for high-quality results. Desp…
A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation
Xianping Ma, Xiaokang Zhang, Man-On Pun +1
Multimodal remote sensing data, acquired from diverse sensors, offer a comprehensive and integrated perspective of the Earth's surface. Leveraging multimodal fusion techniques, sem…
PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery
Libo Wang, Dongxu Li, Sijun Dong +3
Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate s…
RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation
Xianping Ma, Xiaokang Zhang, Man-On Pun
Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, there are some significant shortcomings for the widely used convolutional neur…