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Complex-Domain Semantic Segmentation of Spacecraft Directly from ISAR Echoes.

Aoxiang Pan^1,2, Yonghua He^1,2, Yonggang Li^1,2

¹Space Engineering University, Beijing 101400, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

This study introduces a novel One-Stop Segmentation (OSS) framework for Inverse Synthetic Aperture Radar (ISAR) spacecraft semantic segmentation. The OSS framework enhances accuracy by directly processing ISAR echoes, improving on-orbit spacecraft maintenance.

Keywords:

Inverse Synthetic Aperture Radar (ISAR)automatic labeling complex domain echoes semantic segmentation

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Published on: December 27, 2012

Area of Science:

Spacecraft engineering
Radar signal processing
Computer vision

Background:

Semantic segmentation of Inverse Synthetic Aperture Radar (ISAR) images is vital for on-orbit spacecraft maintenance.
Conventional methods lose scattering information and require extensive manual annotation.
Existing approaches inadequately utilize spacecraft structural priors for accurate segmentation.

Purpose of the Study:

To develop a novel complex-domain semantic segmentation framework for ISAR echoes.
To address limitations of existing ISAR semantic segmentation methods, including information loss and high annotation costs.
To improve the accuracy and efficiency of spacecraft semantic segmentation using ISAR data.

Main Methods:

Proposed a One-Stop Segmentation (OSS) framework utilizing ISAR echoes directly.
Introduced an Automatic ISAR Labeling (AIL) method based on ISAR scattering characteristics.
Developed the One-Stop Segmentation Network (OSSNet) with a Domain Alignment Module (DAM) and Multi-Perspective Attention (MPA) framework (including SCA and SBA modules).

Main Results:

The OSS framework achieved a mean Intersection over Union (mIoU) of 92.13%.
The framework obtained a mean F1-score of 95.75% in ISAR spacecraft semantic segmentation.
Demonstrated superior performance compared to existing methods on a simulated ground-based radar dataset.

Conclusions:

The proposed OSS framework effectively performs semantic segmentation directly on ISAR echoes, preserving crucial information.
OSSNet, with its DAM and MPA modules, significantly enhances segmentation accuracy by leveraging structural priors.
The framework offers a more efficient and accurate solution for intelligent safety maintenance of on-orbit spacecraft.