IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition

• URL: http://arxiv.org/abs/2410.05820v1
• Date: Tue, 8 Oct 2024 08:49:47 GMT
• Title: IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition
• Authors: George Karantaidis, Athanasios Pantsios, Yiannis Kompatsiaris, Symeon Papadopoulos,
• Abstract summary: Models' tendency to forget old knowledge when learning new tasks, known as catastrophic forgetting, remains an open challenge. In this paper, an incremental learning framework, called IncSAR, is proposed to mitigate catastrophic forgetting in SAR target recognition. IncSAR comprises a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in individual branches combined through a late-fusion strategy.
• Score: 7.9330990800767385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning techniques have been successfully applied in Synthetic Aperture Radar (SAR) target recognition in static scenarios relying on predefined datasets. However, in real-world scenarios, models must incrementally learn new information without forgetting previously learned knowledge. Models' tendency to forget old knowledge when learning new tasks, known as catastrophic forgetting, remains an open challenge. In this paper, an incremental learning framework, called IncSAR, is proposed to mitigate catastrophic forgetting in SAR target recognition. IncSAR comprises a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in individual branches combined through a late-fusion strategy. A denoising module, utilizing the properties of Robust Principal Component Analysis (RPCA), is introduced to alleviate the speckle noise present in SAR images. Moreover, a random projection layer is employed to enhance the linear separability of features, and a Linear Discriminant Analysis (LDA) approach is proposed to decorrelate the extracted class prototypes. Experimental results on the MSTAR and OpenSARShip benchmark datasets demonstrate that IncSAR outperforms state-of-the-art approaches, leading to an improvement from $98.05\%$ to $99.63\%$ in average accuracy and from $3.05\%$ to $0.33\%$ in performance dropping rate.

Related papers

• Reciprocal Point Learning Network with Large Electromagnetic Kernel for SAR Open-Set Recognition [6.226365654670747]
  Open Set Recognition (OSR) aims to categorize known classes while denoting unknown ones as "unknown" To enhance open-set SAR classification, a method called scattering kernel with reciprocal learning network is proposed. Proposal is made to design convolutional kernels based on large-sized attribute scattering center models.
  arXiv  Detail & Related papers  (2024-11-07T13:26:20Z)
• EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition [4.251056028888424]
  This study proposes a physically explainable framework for complex-valued SAR image recognition. The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings. The proposed method possesses a strong physical decision logic, high physical explainability and robustness, as well as excellent de-aliasing capabilities.
  arXiv  Detail & Related papers  (2024-10-13T07:04:49Z)
• IRASNet: Improved Feature-Level Clutter Reduction for Domain Generalized SAR-ATR [8.857297839399193]
  This study proposes a framework particularly designed for domain-generalized SAR-ATR called IRASNet. IRASNet enables effective feature-level clutter reduction and domain-invariant feature learning. IRASNet not only enhances performance but also significantly improves feature-level clutter reduction, making it a valuable advancement in the field of radar image pattern recognition.
  arXiv  Detail & Related papers  (2024-09-25T11:53:58Z)
• ACTRESS: Active Retraining for Semi-supervised Visual Grounding [52.08834188447851]
  A previous study, RefTeacher, makes the first attempt to tackle this task by adopting the teacher-student framework to provide pseudo confidence supervision and attention-based supervision. This approach is incompatible with current state-of-the-art visual grounding models, which follow the Transformer-based pipeline. Our paper proposes the ACTive REtraining approach for Semi-Supervised Visual Grounding, abbreviated as ACTRESS.
  arXiv  Detail & Related papers  (2024-07-03T16:33:31Z)
• Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets [0.0]
  We propose a novel adaptive learning rate scheduling strategy tailored for the architecture parameters of DARTS. Our approach dynamically adjusts the learning rate of the architecture parameters based on the training epoch, preventing the disruption of well-trained representations.
  arXiv  Detail & Related papers  (2024-06-11T07:32:25Z)
• SARatrX: Towards Building A Foundation Model for SAR Target Recognition [22.770010893572973]
  We make the first attempt towards building a foundation model for SAR ATR, termed SARatrX. SARatrX learns generalizable representations via self-supervised learning (SSL) and provides a basis for label-efficient model adaptation to generic SAR target detection and classification tasks. Specifically, SARatrX is trained on 0.18 M unlabelled SAR target samples, which are curated by combining contemporary benchmarks and constitute the largest publicly available dataset till now.
  arXiv  Detail & Related papers  (2024-05-15T14:17:44Z)
• Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition [7.858656052565242]
  This paper comprehensively benchmarks several advanced deep learning models for SAR ATR with multiple distinct SAR imagery datasets. We evaluate and compare the five classifiers concerning their classification accuracy runtime performance in terms of inference throughput and analytical performance. No clear model winner emerges from all of our chosen metrics and a one model rules all case is doubtful in the domain of SAR ATR.
  arXiv  Detail & Related papers  (2023-12-12T02:20:39Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Class Anchor Margin Loss for Content-Based Image Retrieval [97.81742911657497]
  We propose a novel repeller-attractor loss that falls in the metric learning paradigm, yet directly optimize for the L2 metric without the need of generating pairs. We evaluate the proposed objective in the context of few-shot and full-set training on the CBIR task, by using both convolutional and transformer architectures.
  arXiv  Detail & Related papers  (2023-06-01T12:53:10Z)
• Implicit Counterfactual Data Augmentation for Robust Learning [24.795542869249154]
  This study proposes an Implicit Counterfactual Data Augmentation method to remove spurious correlations and make stable predictions. Experiments have been conducted across various biased learning scenarios covering both image and text datasets.
  arXiv  Detail & Related papers  (2023-04-26T10:36:40Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Learning class prototypes from Synthetic InSAR with Vision Transformers [2.41710192205034]
  Detection of early signs of volcanic unrest is critical for assessing volcanic hazard. We propose a novel deep learning methodology that exploits a rich source of synthetically generated interferograms. We report detection accuracy that surpasses the state of the art on volcanic unrest detection.
  arXiv  Detail & Related papers  (2022-01-09T14:03:00Z)
• Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution [80.93870349019332]
  We propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes.
  arXiv  Detail & Related papers  (2021-11-27T15:38:57Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need [146.6814176602689]
  We show that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We use this correlation to boost the performance of the cross-entropy OSR 'baseline' by improving its closed-set accuracy. We also construct new benchmarks which better respect the task of detecting semantic novelty.
  arXiv  Detail & Related papers  (2021-10-12T17:58:59Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)
• Reparameterized Variational Divergence Minimization for Stable Imitation [57.06909373038396]
  We study the extent to which variations in the choice of probabilistic divergence may yield more performant ILO algorithms. We contribute a re parameterization trick for adversarial imitation learning to alleviate the challenges of the promising $f$-divergence minimization framework. Empirically, we demonstrate that our design choices allow for ILO algorithms that outperform baseline approaches and more closely match expert performance in low-dimensional continuous-control tasks.
  arXiv  Detail & Related papers  (2020-06-18T19:04:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.