3.2k citations · 6k across the 61 of their papers we have counts for
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R-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
Qingpeng Li, Lichao Mou, Qizhi Xu +2
Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer…
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…
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
Lichao Mou, Xiao Xiang Zhu
Object detection and semantic segmentation are two main themes in object retrieval from high-resolution remote sensing images, which have recently achieved remarkable performance b…
RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images
Lichao Mou, Xiao Xiang Zhu
Semantic segmentation in high resolution remote sensing images is a fundamental and challenging task. Convolutional neural networks (CNNs), such as fully convolutional network (FCN…
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Lichao Mou, Lorenzo Bruzzone, Xiao Xiang Zhu
Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional…
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
Lichao Mou, Xiao Xiang Zhu
In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed prob…
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
Lloyd H. Hughes, Michael Schmitt, Lichao Mou +2
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolut…