Feature Enhancement Using Multi-Baseline SAR Interferometry-Correlated Synthesis Images for Power Transmission Tower Detection in Mountain Layover Area

The detection performance of power transmission towers in mountainous areas using SAR amplitude images is obviously influenced by the strong layover background (mainly including vegetation and soil) clutter interference around the towers. In this paper, power transmission tower detection in a mounta...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 15; p. 3823
Main Authors Wu, Baolong, Wang, Haonan, Chen, Jianlai
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2023
Subjects
Amplitudes
Clutter
Coherence
Comparative analysis
Correlation
Deep learning
Detectors
Image enhancement
InSAR
Interference
Interferometry
Mountain regions
Mountainous areas
Mountains
object detection
power transmission tower
Remote sensing
Statistical analysis
Synthesis
synthetic aperture radar (SAR)
Teaching methods
Theoretical analysis
Transmission towers
Vegetation
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Summary:The detection performance of power transmission towers in mountainous areas using SAR amplitude images is obviously influenced by the strong layover background (mainly including vegetation and soil) clutter interference around the towers. In this paper, power transmission tower detection in a mountainous layover area, using single-baseline SAR interferometry coherence images, which show better feature enhancement effectiveness compared to SAR amplitude images, is presented. Moreover, a novel feature enhancement method, that of generating multi-baseline SAR interferometry-correlated synthesis images for power transmission tower detection in a mountain layover area, is proposed. It demonstrates better feature enhancement (layover background cluster suppression) than that using single-baseline SAR interferometry coherence images. Theoretical analysis illustrates that the mountainous layover background clutter interference can be suppressed in the proposed single-baseline/multi-baseline SAR interferometry-correlated synthesis image. Experiments including over 12 repeat-pass TerraSAR-X staring spotlight mode acquisitions were conducted, and the results demonstrate that the detection performance with the use of multi-baseline SAR interferometry-correlated synthesis images showed an improvement of more than 43.6%, compared with the traditional method of using SAR amplitude images when benchmark deep learning-based detectors are used, i.e., Faster RCNN and YOLOv7.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15153823