TY - GEN
T1 - A Lightweight and Fast Method for Water and Airport Segmentation in SAR Images
AU - Qiu, Yu
AU - Zou, Bin
AU - Zhang, Lamei
N1 - Publisher Copyright:
© VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.
PY - 2024
Y1 - 2024
N2 - In large scene SAR images, the extraction of regions of interest is crucial for improving the accuracy of target detection. Airports and water bodies within these scenes often contain aircraft and ships, which are primary targets for detection. SAR images exhibit similar scattering characteristics for airport runways and water bodies. In light of this, our paper presents a lightweight and fast segmentation method applicable to both airport and water regions(LFSAW). Firstly, we construct a lightweight and efficient concatenate module (LECM) for rapid feature extraction. Subsequently, we create a specialized module for SAR images that extracts edge information (DIEM) from ground truth and utilizes it for supervision while simultaneously enhancing the segmentation performance by extracting detailed information. Finally, we employ a multi-task loss function to jointly supervise the edge detail and segmentation information. Furthermore, to facilitate simultaneous segmentation of airport and water regions, we build a dataset comprising 7931 image slices designed specifically for airport-water body segmentation tasks.
AB - In large scene SAR images, the extraction of regions of interest is crucial for improving the accuracy of target detection. Airports and water bodies within these scenes often contain aircraft and ships, which are primary targets for detection. SAR images exhibit similar scattering characteristics for airport runways and water bodies. In light of this, our paper presents a lightweight and fast segmentation method applicable to both airport and water regions(LFSAW). Firstly, we construct a lightweight and efficient concatenate module (LECM) for rapid feature extraction. Subsequently, we create a specialized module for SAR images that extracts edge information (DIEM) from ground truth and utilizes it for supervision while simultaneously enhancing the segmentation performance by extracting detailed information. Finally, we employ a multi-task loss function to jointly supervise the edge detail and segmentation information. Furthermore, to facilitate simultaneous segmentation of airport and water regions, we build a dataset comprising 7931 image slices designed specifically for airport-water body segmentation tasks.
UR - https://www.scopus.com/pages/publications/85193940033
M3 - 会议稿件
AN - SCOPUS:85193940033
T3 - Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
SP - 300
EP - 305
BT - EUSAR 2024 - 15th European Conference on Synthetic Aperture Radar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th European Conference on Synthetic Aperture Radar, EUSAR 2024
Y2 - 23 April 2024 through 26 April 2024
ER -