838 citations · 1.1k across the 16 of their papers we have counts for
16 papers
Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality
Danfeng Hong, Chenyu Li, Xuyang Li +2
Remote sensing (RS) techniques are increasingly crucial for deepening our understanding of the planet. As the volume and diversity of RS data continue to grow exponentially, there…
KANO: Kolmogorov-Arnold Neural Operator for Image Super-Resolution
Chenyu Li, Danfeng Hong, Bing Zhang +2
The highly nonlinear degradation process, complex physical interactions, and various sources of uncertainty render single-image Super-resolution (SR) a particularly challenging tas…
Any-Optical-Model: A Universal Foundation Model for Optical Remote Sensing
Xuyang Li, Chenyu Li, Danfeng Hong
Optical satellites, with their diverse band layouts and ground sampling distances, supply indispensable evidence for tasks ranging from ecosystem surveillance to emergency response…
MambaX: Image Super-Resolution with State Predictive Control
Chenyu Li, Danfeng Hong, Bing Zhang +3
Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing th…
Hyperspectral Imaging
Danfeng Hong, Chenyu Li, Naoto Yokoya +6
Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, c…
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
Jie Deng, Danfeng Hong, Chenyu Li +1
We propose a novel joint framework by integrating super-resolution and segmentation, called JointSeg, which enables the generation of 1-meter ISA maps directly from freely availabl…
FlexiMo: A Flexible Remote Sensing Foundation Model
Xuyang Li, Chenyu Li, Pedram Ghamisi +1
The rapid expansion of multi-source satellite imagery drives innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness di…
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Chenyu Li, Danfeng Hong, Bing Zhang +4
Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphology is inherently complex, with…
A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
Lei Ding, Danfeng Hong, Maofan Zhao +6
In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sen…
SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing
Xuyang Li, Chenyu Li, Gemine Vivone +1
Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particul…
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification
Jing Yao, Danfeng Hong, Chenyu Li +1
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies fro…
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan +7
Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine…
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
Chenyu Li, Bing Zhang, Danfeng Hong +2
Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from low resolution, simple background, and small size of the detection data. These factors also limit the p…
SpectralGPT: Spectral Remote Sensing Foundation Model
Danfeng Hong, Bing Zhang, Xuyang Li +11
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. Whil…
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Danfeng Hong, Bing Zhang, Hao Li +7
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on indivi…
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Danfeng Hong, Jing Yao, Deyu Meng +2
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perf…