15 citations · 17 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
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…