L
Lei Guo
Researcher at Chongqing University of Posts and Telecommunications
Publications - 1837
Citations - 37002
Lei Guo is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 75, co-authored 1589 publications receiving 27943 citations. Previous affiliations of Lei Guo include Chinese Ministry of Education & AT&T.
Papers
More filters
Journal ArticleDOI
When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs
TL;DR: This paper proposes a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification and comprehensively evaluates the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models.
Journal ArticleDOI
Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning
TL;DR: A novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning by jointly integrating saliency, intraclass compactness, and interclass separability in a Bayesian framework.
Journal ArticleDOI
Multi-class geospatial object detection and geographic image classification based on collection of part detectors
TL;DR: Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.
Journal ArticleDOI
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
TL;DR: This article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers and discusses the main challenges of remote sensing images classification and survey.
Proceedings ArticleDOI
Measurements, analysis, and modeling of BitTorrent-like systems
TL;DR: An analysis of representative Bit-Torrent traffic provides several new findings regarding the limitations of BitTorrent systems: due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded.