Low-Frequency Ultrawideband Synthetic Aperture Radar Foliage-Concealed Target Change Detection Strategy Based on Image Stacks

Low-frequency ultrawideband synthetic aperture radar (UWB SAR) has high-resolution imaging and foliage-penetrating ability, which can detect the foliage-concealed target. However, due to the jungle detection environment and the low-frequency UWB SAR characteristics, there are often some nontarget st...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.19817-19830
Main Authors: Xie, Hongtu, Yi, Shiliang, He, Jinfeng, Zhang, Yuanjie, Lu, Zheng, Zhu, Nannan
Format: Article
Language:English
Subjects:
Change detection
Detection algorithms
Feature extraction
Filtering
foliage-concealed target
Gamma distribution
image stacks
Linear regression
low frequency
Radar polarimetry
Radiometry
Remote sensing
Scattering
Synthetic aperture radar
synthetic aperture radar (SAR)
ultrawideband (UWB)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Low-frequency ultrawideband synthetic aperture radar (UWB SAR) has high-resolution imaging and foliage-penetrating ability, which can detect the foliage-concealed target. However, due to the jungle detection environment and the low-frequency UWB SAR characteristics, there are often some nontarget strong scattering points in low-frequency UWB SAR images, which may increase the difficulty of foliage-concealed target change detection. To improve the change detection rate of the foliage-concealed target, a foliage-concealed target change detection strategy based on image stacks in low-frequency UWB SAR images is proposed. In image preprocessing, a relative radiometric correction method based on the bidirectional linear regression model is presented, which can eliminate the low-frequency UWB SAR image changes caused by nontarget factors. Besides, in change detection processing, multiple difference images are first obtained by subtracting the image to be detected from multiple reference images. Then, the Gaussian probability density function is used to model the distribution of the amplitude of these difference images. Finally, the generalized likelihood ratio test is used for target change detection, which effectively suppresses the interference, such as tree trunk clutter. Experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed strategy, which can improve the change detection probability of the foliage-concealed target with the lower false alarm rate.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3477602