most citedAdaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

eess.IV2025

Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification

Yikang Zhao, Feng Gao, Xuepeng Jin +2

Multi-source data classification is a critical yet challenging task for remote sensing image interpretation. Existing methods lack adaptability to diverse land cover types when mod…

cs.CV2025

Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues

Yimei Liu, Yakun Ju, Yuan Rao +4

Three-dimensional digital urban reconstruction from multi-view aerial images is a critical application where deep multi-view stereo (MVS) methods outperform traditional techniques.…

eess.IV2025

Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification

Feng Gao, Sheng Liu, Chuanzheng Gong +4

Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification by leveraging the complementary information from multipl…

eess.IV20251 cited

Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation

Feng Gao, Miao Fu, Jingchao Cao +2

Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based met…

eess.IV2025

Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction

Jialiang Zhang, Feng Gao, Yanhai Gan +2

Accurately forecasting sea ice concentration (SIC) in the Arctic is critical to global ecosystem health and navigation safety. However, current methods still is confronted with two…

eess.IV2024

Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection

Ziyi Wang, Feng Gao, Junyu Dong +1

Recently Transformer-based hyperspectral image (HSI) change detection methods have shown remarkable performance. Nevertheless, existing attention mechanisms in Transformers have li…