A Survey on SAR ship classification using Deep Learning

• URL: http://arxiv.org/abs/2503.11906v1
• Date: Fri, 14 Mar 2025 22:19:24 GMT
• Title: A Survey on SAR ship classification using Deep Learning
• Authors: Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Emanuele Salerno,
• Abstract summary: Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification.<n>This survey comprehensively analyzes the diverse DL techniques employed in this domain.
• Score: 5.175998799554875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. This survey comprehensively analyzes the diverse DL techniques employed in this domain. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and fostering interdisciplinary collaborations to improve DL model performance. This survey establishes a first-of-its-kind taxonomy for categorizing relevant research based on DL models, handcrafted feature use, SAR attribute utilization, and the impact of fine-tuning. We discuss the methodologies used in SAR ship classification tasks and the impact of different techniques. Finally, the survey explores potential avenues for future research, including addressing data scarcity, exploring novel DL architectures, incorporating interpretability techniques, and establishing standardized performance metrics. By addressing these challenges and leveraging advancements in DL, researchers can contribute to developing more accurate and efficient ship classification systems, ultimately enhancing maritime surveillance and related applications.

Related papers

• Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey [43.75849983150303]
  As datasets become more complex, traditional anomaly detection methods struggle to capture intricate patterns.<n>Deep learning has made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data.<n>This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.
  arXiv  Detail & Related papers  (2025-03-17T14:04:48Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs [5.961207817077044]
  We propose a novel self-supervised learning framework based on masked Siamese Vision Transformers to create a General SAR Feature Extractor coined SAFE. Our method leverages contrastive learning principles to train a model on unlabeled SAR data, extracting robust and generalizable features. We introduce tailored data augmentation techniques specific to SAR imagery, such as sub-aperture decomposition and despeckling. Our network competes with or surpasses other state-of-the-art methods in few-shot classification and segmentation tasks, even without being trained on the sensors used for the evaluation.
  arXiv  Detail & Related papers  (2024-06-30T23:11:20Z)
• Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification [59.99976102069976]
  Fine-grained ship classification in remote sensing (RS-FGSC) poses a significant challenge due to the high similarity between classes and the limited availability of labeled data.<n>Recent advancements in large pre-trained Vision-Language Models (VLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning.<n>This study delves into harnessing the potential of VLMs to enhance classification accuracy for unseen ship categories.
  arXiv  Detail & Related papers  (2024-03-13T05:48:58Z)
• Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey [2.716339075963185]
  Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR) ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources. Advanced DL techniques like deep transfer learning (DTL), federated learning (FL), and reinforcement learning (RL) address these issues.
  arXiv  Detail & Related papers  (2024-03-02T16:25:42Z)
• Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture [23.375515181854254]
  Self-Supervised Learning (SSL) methods can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data. SSL aims to construct supervision signals directly from the data, which minimizes the need for expensive expert annotation. This study investigates an effective SSL method for SAR ATR, which can pave the way for a foundation model in SAR ATR.
  arXiv  Detail & Related papers  (2023-11-26T01:05:55Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Deep Depth Completion: A Survey [26.09557446012222]
  We provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies. We present a quantitative comparison of model performance on two widely used benchmark datasets, including an indoor and an outdoor dataset.
  arXiv  Detail & Related papers  (2022-05-11T08:24:00Z)
• Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects [0.6091702876917281]
  Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic.
  arXiv  Detail & Related papers  (2021-01-15T13:59:22Z)

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.