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Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification
Yuwei Zhao, Chuanzheng Gong, Baogui Huan +3
Multi-source remote sensing image classification has attracted increasing attention due to the complementary spectral, structural, and geometric information. However, existing meth…
Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
Tengya Zhang, Feng Gao, Lin Qi +2
Hyperspectral image super-resolution is essential for enhancing the spatial fidelity of HSI data, yet existing deep learning methods often struggle with substantial spectral redund…
Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification
Chuanzheng Gong, Feng Gao, Junyan Lin +2
Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their effective fusion remains challeng…
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…
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…
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…
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…
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…
Hierarchical Attention and Parallel Filter Fusion Network for Multi-Source Data Classification
Han Luo, Feng Gao, Junyu Dong +1
Hyperspectral image (HSI) and synthetic aperture radar (SAR) data joint classification is a crucial and yet challenging task in the field of remote sensing image interpretation. Ho…
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
Feng Gao, Xuepeng Jin, Xiaowei Zhou +2
In the field of multi-source remote sensing image classification, remarkable progress has been made by using Convolutional Neural Network (CNN) and Transformer. Recently, Mamba-bas…
Sparse Focus Network for Multi-Source Remote Sensing Data Classification
Xuepeng Jin, Junyan Lin, Feng Gao +2
Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors. Existing multi-source data classification methods…
Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification
Junyan Lin, Xuepeng Jin, Feng Gao +2
Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequatel…
SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification
Junyan Lin, Feng Gao, Xiaocheng Shi +2
Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. Existing MIM-based methods mostly focus on spatial feature mo…