# MTSGL: Multi-Task Structure Guided Learning for Robust and Interpretable SAR Aircraft Recognition

arXiv: https://arxiv.org/abs/2504.16467 · HTML: https://mlsanity.com/paper/2504.16467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 13 of 13)

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- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery](https://mlsanity.com/paper/2201.07124/) (2022)
- [When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation](https://mlsanity.com/paper/2308.07093/) (2023)

## Abstract (arXiv)

Aircraft recognition in synthetic aperture radar (SAR) imagery is a fundamental mission in both military and civilian applications. Recently deep learning (DL) has emerged a dominant paradigm for its explosive performance on extracting discriminative features. However, current classification algorithms focus primarily on learning decision hyperplane without enough comprehension on aircraft structural knowledge. Inspired by the fined aircraft annotation methods for optical remote sensing images (RSI), we first introduce a structure-based SAR aircraft annotations approach to provide structural and compositional supplement information. On this basis, we propose a multi-task structure guided learning (MTSGL) network for robust and interpretable SAR aircraft recognition. Besides the classification task, MTSGL includes a structural semantic awareness (SSA) module and a structural consistency regularization (SCR) module. The SSA is designed to capture structure semantic information, which is conducive to gain human-like comprehension of aircraft knowledge. The SCR helps maintain the geometric consistency between the aircraft structure in SAR imagery and the proposed annotation. In this process, the structural attribute can be disentangled in a geometrically meaningful manner. In conclusion, the MTSGL is presented with the expert-level aircraft prior knowledge and structure guided learning paradigm, aiming to comprehend the aircraft concept in a way analogous to the human cognitive process. Extensive experiments are conducted on a self-constructed multi-task SAR aircraft recognition dataset (MT-SARD) and the effective results illustrate the superiority of robustness and interpretation ability of the proposed MTSGL.