Paper Info
Title
Imaging Parameters-Considered Slender Target Detection in Optical Satellite Images
Abstract
The existing slender target detection methods based on optical satellite images are greatly affected by the satellite perspective and the solar perspective. Due to limited data sources, it is difficult to implement a fully data-driven approach. This work introduces the imaging parameters of optical satellite images, which greatly reduces the influence of the satellite perspectives and the solar perspectives, and reduces the demand for the amount of data. We improve the oriented bounding box (OBB) detector based on faster R-CNN (region convolutional neural networks) and propose an imaging parameters-considered detector (IPC-Det) which is more suitable for our task. Specifically, in the first stage, the umbra and the shadow are extracted by horizontal bounding box (HBB), respectively, and then the matching of the umbra and the shadow is realized according to the imaging parameters. In the second stage, the paired umbra and shadow features are used to complete the classification and regression, and the target is obtained by OBB. In experiments, after introducing imaging parameters, our detection accuracy is improved by 3.9% (up to 87.5%), proving that this work is a successful attempt to introduce imaging parameters for slender target detection.
Year
DOI
Venue
2022
10.3390/rs14061385
REMOTE SENSING
Keywords
DocType
Volume
optical satellite image, imaging parameters, slender targets, object detection
Journal
14
Issue
ISSN
Citations 
6
2072-4292
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhaoyang Huang101.01
Feng Wang298.41
Hongjian You310317.44
Yuxin Hu416.77