75 citations · 89 across the 8 of their papers we have counts for
8 papers · 1 filter
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
Guanchun Wang, Xiangrong Zhang, Yifei Zhang +4
Unsupervised anomaly detection in hyperspectral images (HSI), aiming to detect unknown targets from backgrounds, is challenging for earth surface monitoring. However, current studi…
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing
Jinyue Zhang, Xiangrong Zhang, Zhongjian Huang +3
Moving object detection (MOD) in remote sensing is significantly challenged by low resolution, extremely small object sizes, and complex noise interference. Current deep learning-b…
EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting
Di Li, Jie Feng, Jiahao Chen +4
Egocentric scenes exhibit frequent occlusions, varied viewpoints, and dynamic interactions compared to typical scene understanding tasks. Occlusions and varied viewpoints can lead…
S4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation
Jie Feng, Tianshu Zhang, Junpeng Zhang +4
Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data t…
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection
Jie Feng, Xiaojian Zhong, Di Li +3
Band selection plays a crucial role in hyperspectral image classification by removing redundant and noisy bands and retaining discriminative ones. However, most existing deep learn…
SMamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
Guanchun Wang, Xiangrong Zhang, Zelin Peng +2
Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedi…
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
Jie Feng, Hao Huang, Junpeng Zhang +3
Mainstreamed weakly supervised road extractors rely on highly confident pseudo-labels propagated from scribbles, and their performance often degrades gradually as the image scenes…
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Xiangrong Zhang, Tianyang Zhang, Guanchun Wang +4
Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep le…