# Physically Explainable CNN for SAR Image Classification

arXiv: https://arxiv.org/abs/2110.14144 · HTML: https://mlsanity.com/paper/2110.14144/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge](https://mlsanity.com/paper/1711.07970/) (2017)
- [Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling](https://mlsanity.com/paper/1911.02682/) (2019)
- [What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs](https://mlsanity.com/paper/1906.01379/) (2019)
- [Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning](https://mlsanity.com/paper/2001.01425/) (2020)
- [Joint Gaussian Processes for Biophysical Parameter Retrieval](https://mlsanity.com/paper/1711.05197/) (2017)

## Built on by (most cited first, 16 of 16)

- [Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2311.15153/) (2023)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [SARATR-X: Toward Building A Foundation Model for SAR Target Recognition](https://mlsanity.com/paper/2405.09365/) (2024)
- [RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation](https://mlsanity.com/paper/2504.03166/) (2025)
- [ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild](https://mlsanity.com/paper/2501.13354/) (2025)
- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [Physics Inspired Hybrid Attention for SAR Target Recognition](https://mlsanity.com/paper/2309.15697/) (2023)
- [Physics-Guided Detector for SAR Airplanes](https://mlsanity.com/paper/2411.12301/) (2024)
- [PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR Images](https://mlsanity.com/paper/2401.02687/) (2024)
- [EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition](https://mlsanity.com/paper/2410.09749/) (2024)
- [Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labels](https://mlsanity.com/paper/2508.07656/) (2025)
- [Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar](https://mlsanity.com/paper/2301.03589/) (2023)
- [Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges](https://mlsanity.com/paper/2306.00303/) (2023)
- [A physics-guided neural network for flooding area detection using SAR imagery and local river gauge observations](https://mlsanity.com/paper/2410.08837/) (2024)
- [Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network](https://mlsanity.com/paper/2507.03315/) (2025)
- [DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model](https://mlsanity.com/paper/2511.07808/) (2025)

## Abstract (arXiv)

Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL). It comprises three parts: (1) explainable models (XM) to provide prior physics knowledge, (2) physics guided network (PGN) to encode the knowledge into physics-aware features, and (3) physics injected network (PIN) to adaptively introduce the physics-aware features into classification pipeline for label prediction. A hybrid Image-Physics SAR dataset format is proposed for evaluation, with both Sentinel-1 and Gaofen-3 SAR data being experimented. The results show that the proposed PGIL substantially improve the classification performance in case of limited labeled data compared with the counterpart data-driven CNN and other pre-training methods. Additionally, the physics explanations are discussed to indicate the interpretability and the physical consistency preserved in the predictions. We deem the proposed method would promote the development of physically explainable deep learning in SAR image interpretation field.