15 citations · 17 across the 9 of their papers we have counts for
9 papers
HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark
Chuguang Zeng, Jingtao Li, Yinhe Liu +1
We present HyperImageNet, a large-scale benchmark for fine-grained hyperspectral land-cover understanding. The dataset contains 26,084 airborne hyperspectral image patches with 224…
Fertility fibres and coproduct coefficients in the LOT Hopf algebra
Zhicheng Zhu, Jingtao Li, Xing Gao
We study fibres of the fertility map from decorated rooted trees to decorated multi-index monomials. For a multi-index of weight , the fibre $\mathcal F_{\math…
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery
Jingtao Li, Yingyi Liu, Xinyu Wang +9
Advanced interpretation of hyperspectral remote sensing images benefits many precise Earth observation tasks. Recently, visual foundation models have promoted the remote sensing in…
HOpenCls: Training Hyperspectral Image Open-Set Classifiers in Their Living Environments
Hengwei Zhao, Xinyu Wang, Zhuo Zheng +2
Hyperspectral image (HSI) open-set classification is critical for HSI classification models deployed in real-world environments, where classifiers must simultaneously classify know…
AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations
Jingtao Li, Qian Zhu, Xinyu Wang +2
Various Earth anomalies have destroyed the stable, balanced state, resulting in fatalities and serious destruction of property. With the advantages of large-scale and precise obser…
Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery
Jingtao Li, Xinyu Wang, Hengwei Zhao +2
Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing…
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery
Hengwei Zhao, Xinyu Wang, Jingtao Li +1
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad p…
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
Jingtao Li, Xinyu Wang, Shaoyu Wang +3
Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based metho…
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
Jingtao Li, Xinyu Wang, Hengwei Zhao +2
Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an imp…