activity
20172021
most citedDeep learning in remote sensing: a review

3.2k citations · 3.3k across the 7 of their papers we have counts for

collaborators

17 papers

cs.CV2021

Segmentation of VHR EO Images using Unsupervised Learning

Sudipan Saha, Lichao Mou, Muhammad Shahzad +1

Semantic segmentation is a crucial step in many Earth observation tasks. Large quantity of pixel-level annotation is required to train deep networks for semantic segmentation. Eart…

cs.CV2021

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…

eess.IV2020

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…

cs.CV20202 cited

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…

cs.CV202030 cited

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…

cs.CV20205 cited

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

Di Hu, Xuhong Li, Lichao Mou +5

Aerial scene recognition is a fundamental task in remote sensing and has recently received increased interest. While the visual information from overhead images with powerful model…