838 citations · 1.1k across the 13 of their papers we have counts for
14 papers
VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation
Danfeng Hong, Chenyu Li, Jocelyn Chanussot
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantl…
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…
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…
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…
SpectralEarth: Training Hyperspectral Foundation Models at Scale
Nassim Ait Ali Braham, Conrad M Albrecht, Julien Mairal +3
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potent…
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan +6
Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffe…
Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
Ian Hough, Loïc Argentier, Ziyang Jiang +6
Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Sinc…
Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives
Matteo Ciotola, Giuseppe Guarino, Gemine Vivone +4
Hyperspectral pansharpening consists of fusing a high-resolution panchromatic band and a low-resolution hyperspectral image to obtain a new image with high resolution in both the s…
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…
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…