193 citations · 671 across the 16 of their papers we have counts for
16 papers
RRSIS: Referring Remote Sensing Image Segmentation
Zhenghang Yuan, Lichao Mou, Yuansheng Hua +1
Localizing desired objects from remote sensing images is of great use in practical applications. Referring image segmentation, which aims at segmenting out the objects to which a g…
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Pu Jin, Lichao Mou, Yuansheng Hua +2
Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automa…
SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
Tianze Yu, Jianzhe Lin, Lichao Mou +3
Most publicly available datasets for image classification are with single labels, while images are inherently multi-labeled in our daily life. Such an annotation gap makes many pre…
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
Yuansheng Hua, Lichao Moua, Jianzhe Lin +2
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to c…
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images
Yuansheng Hua, Lichao Mou, Pu Jin +1
Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into…
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
Lichao Mou, Sudipan Saha, Yuansheng Hua +3
Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model train…
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Yuansheng Hua, Diego Marcos, Lichao Mou +2
Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-…
CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
Yao Sun, Yuansheng Hua, Lichao Mou +1
Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging…
Instance segmentation of buildings using keypoints
Qingyu Li, Lichao Mou, Yuansheng Hua +4
Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods oft…
Deep Learning Meets SAR
Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali +6
Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Syntheti…
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Di Hu, Lichao Mou, Qingzhong Wang +4
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail…
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
Lichao Mou, Yuansheng Hua, Pu Jin +1
Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and unders…
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu +14
Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images…
Relation Network for Multi-label Aerial Image Classification
Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of the…
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Lichao Mou, Yuansheng Hua, Xiao Xiang Zhu
Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields cause…
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
Aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them…