Search

Your search keyword '"Xiong, Boli"' showing total 107 results
Start Over Author "Xiong, Boli"
107 results on '"Xiong, Boli"'

3. Statistical Modeling Methods of Single-channel Complex-valued SAR Images for Ship Detection

Author

LENG Xiangguang, JI Kefeng, XIONG Boli, and KUANG Gangyao

Subjects
single-channel sar image, ship detection, complex-valued information, statistical modeling, Electricity and magnetism, QC501-766
Abstract

Synthetic Aperture Radar (SAR), which features rich imaging modes, wide coverage, and high resolution, is an effective technique for long-term, dynamic, and large-scale monitoring of the ocean. Under the assumption of fully developed speckle, traditional ship detection methods in single-channel SAR images focus mainly on amplitude information. Since conventional assumptions are not strictly true in high-resolution situations, this prevents the full investigation of phase or complex-valued information in single-channel SAR images. In this paper, with a focus on ship detection applications, we categories the methods used in the statistical modeling of single-channel complexvalued SAR images as amplitude-, phase-, or complex-valued-based. After providing a brief overview of amplitude statistical modeling methods, we focus on phase and complex-valued statistical modeling methods of single-channel SAR images, describing their modeling processes and parameter estimation methods. We then present the results of our recent ship detection research based on complex-valued statistical information in single-channel SAR images and make suggestions regarding future research.

Published
2020
Full Text
View/download PDF

31. A cascaded three-look network for aircraft detection in SAR images.

Author

Zhang, Linbin, Li, Chuyin, Zhao, Lingjun, Xiong, Boli, Quan, Sinong, and Kuang, Gangyao

Subjects
ARTIFICIAL neural networks, OBJECT recognition (Computer vision), SYNTHETIC aperture radar, RUNWAYS (Aeronautics), IMAGE analysis, RADARSAT satellites, OPTICAL images, REMOTE sensing
Abstract

Aircraft detection in synthetic aperture radar (SAR) images is a tough problem in remote sensing image interpretation, due to different scattering characteristics and scales of aircrafts as well as the influences of airport metal objects. Although deep convolutional neural networks perform well in object detection on optical images, the application of deep learning for SAR image aircraft detection has not been adequately studied. In this article, a cascaded three-look network is proposed to locate aircrafts in SAR images. The detection networks for the airport and aircrafts are modified separately in region proposal, which combines the superiorities of Faster R-CNN in object detection and residual units in feature extraction. The network contains three looks in noticing airport, detecting aircraft and extracting airfield runway. Three looks are designed not only to reduce the detection range and false alarms but also to elevate the detection precision. Experiments conducted on test data demonstrate that the proposed method can achieve 0.67 in F1 score, which outperforms the compared method. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results