93 citations · 220 across the 9 of their papers we have counts for
11 papers
See the Future, Classify the Present: Stochastic Latent World Modeling for Hyperspectral Images
Muhammad Ahmad, Gemine Vivone, Ronny Hänsch +3
Hyperspectral image classification (HSIC) relies on static spatial-spectral representations learned from labeled samples, limiting its ability to model structured dependencies in t…
A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images
Jianzheng Wang, Huan Ni, Xiaonan Niu +2
The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SAM3-based open-vocabulary sem…
Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
Shanwen Wang, Xin Sun, Danfeng Hong +2
Although semi-supervised semantic segmentation () utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data…
Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images
Shanwen Wang, Xin Sun, Danfeng Hong +1
The semi-supervised semantic segmentation (S4) can learn rich visual knowledge from low-cost unlabeled images. However, traditional S4 architectures all face the challenge of low-q…
Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals
Huan Ni, Qingshan Liu, Xiaonan Niu +3
Cross-domain few-shot segmentation (CD-FSS) aims to segment unseen categories with very limited samples while alleviating the negative effects of domain shift between the source an…
A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges
Xing Hu, Haodong Chen, Qianqian Duan +1
With the global population increasing and arable land resources becoming increasingly limited, smart and precision agriculture have emerged as essential directions for sustainable…
S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images
Lei Ding, Xibing Zuo, Danfeng Hong +4
Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images remains a difficult challenge due to the inherent spatio-temporal complexity within data, and the heter…
Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama +4
Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges…
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…
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…
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…