3.2k citations · 9.7k across the 58 of their papers we have counts for
58 papers
Binary Change Guided Hyperspectral Multiclass Change Detection
Meiqi Hu, Chen Wu, Bo Du +1
Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent rese…
Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images
Kunping Yang, Xin-Yi Tong, Gui-Song Xia +2
Targeting at depicting land covers with pixel-wise semantic categories, semantic segmentation in remote sensing images needs to portray diverse distributions over vast geographical…
Self-Ensembling GAN for Cross-Domain Semantic Segmentation
Yonghao Xu, Fengxiang He, Bo Du +2
Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level…
LUAI Challenge 2021 on Learning to Understand Aerial Images
Gui-Song Xia, Jian Ding, Ming Qian +33
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in a…
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
Huanfeng Shen, Menghui Jiang, Jie Li +3
In the fields of image restoration and image fusion, model-driven methods and data-driven methods are the two representative frameworks. However, both approaches have their respect…
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
Zhuo Zheng, Ailong Ma, Liangpei Zhang +1
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it…
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification
Qiqi Zhu, Weihuan Deng, Zhuo Zheng +5
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large pa…
Robust Self-Ensembling Network for Hyperspectral Image Classification
Yonghao Xu, Bo Du, Liangpei Zhang
Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires…
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Danfeng Hong, Wei He, Naoto Yokoya +5
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to proce…
Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
Mengxi Liu, Qian Shi, Andrea Marinoni +3
Change detection, which aims to distinguish surface changes based on bi-temporal images, plays a vital role in ecological protection and urban planning. Since high resolution (HR)…
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion
Dong Chu, Huanfeng Shen, Xiaobin Guan +4
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods…
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
Jiang He, Jie Li, Qiangqiang Yuan +2
Hyperspectral images are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high spatial resolution (HR) hyperspectral images from HR multi…
Hyperspectral Anomaly Change Detection Based on Auto-encoder
Meiqi Hu, Chen Wu, Liangpei Zhang +1
With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis and m…
Semantic Change Detection with Asymmetric Siamese Networks
Kunping Yang, Gui-Song Xia, Zicheng Liu +4
Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem…
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
Chen Wu, Yinong Guo, Haonan Guo +5
In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restric…
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
Yang Long, Gui-Song Xia, Shengyang Li +5
The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there…
Holistically-Attracted Wireframe Parsing
Nan Xue, Tianfu Wu, Song Bai +4
This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed metho…
Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data
Huanfeng Shen, Yun Jiang, Tongwen Li +3
Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep…
Learning Regional Attraction for Line Segment Detection
Nan Xue, Song Bai, Fu-Dong Wang +4
This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map…
Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks
Hongruixuan Chen, Chen Wu, Bo Du +1
Very-high-resolution (VHR) images can provide abundant ground details and spatial geometric information. Change detection in multi-temporal VHR images plays a significant role in u…
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Bo Du, Zengmao Wang, Lefei Zhang +2
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also h…
Exploring Representativeness and Informativeness for Active Learning
Bo Du, Zengmao Wang, Lefei Zhang +4
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an…
Learning Attraction Field Representation for Robust Line Segment Detection
Nan Xue, Song Bai, Fudong Wang +3
This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region co…
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
Bo Du, Lixiang Ru, Chen Wu +1
Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detectio…