FewSAR: A Few-shot SAR Image Classification Benchmark

• URL: http://arxiv.org/abs/2306.09592v1
• Date: Fri, 16 Jun 2023 02:35:00 GMT
• Title: FewSAR: A Few-shot SAR Image Classification Benchmark
• Authors: Rui Zhang, Ziqi Wang, Yang Li, Jiabao Wang, Zhiteng Wang
• Abstract summary: Few-shot learning is one of the significant and hard problems in the field of image classification. FewSAR consists of an open-source Python code library of 15 classic methods in three categories for few-shot SAR image classification. By analyzing the quantitative results and runtime under the same setting, we observe that the accuracy of metric learning methods can achieve the best results.
• Score: 17.24173332659616
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Few-shot learning (FSL) is one of the significant and hard problems in the field of image classification. However, in contrast to the rapid development of the visible light dataset, the progress in SAR target image classification is much slower. The lack of unified benchmark is a key reason for this phenomenon, which may be severely overlooked by the current literature. The researchers of SAR target image classification always report their new results on their own datasets and experimental setup. It leads to inefficiency in result comparison and impedes the further progress of this area. Motivated by this observation, we propose a novel few-shot SAR image classification benchmark (FewSAR) to address this issue. FewSAR consists of an open-source Python code library of 15 classic methods in three categories for few-shot SAR image classification. It provides an accessible and customizable testbed for different few-shot SAR image classification task. To further understanding the performance of different few-shot methods, we establish evaluation protocols and conduct extensive experiments within the benchmark. By analyzing the quantitative results and runtime under the same setting, we observe that the accuracy of metric learning methods can achieve the best results. Meta-learning methods and fine-tuning methods perform poorly on few-shot SAR images, which is primarily due to the bias of existing datasets. We believe that FewSAR will open up a new avenue for future research and development, on real-world challenges at the intersection of SAR image classification and few-shot deep learning. We will provide our code for the proposed FewSAR at https://github.com/solarlee/FewSAR.

Related papers

• Can SAR improve RSVQA performance? [1.6249398255272318]
  We study whether Synthetic Aperture Radar (SAR) images can be beneficial to this field. We investigate the classification results of SAR alone and investigate the best method to extract information from SAR data. In the last phase, we investigate how SAR images and a combination of different modalities behave in RSVQA compared to a method only using optical images.
  arXiv  Detail & Related papers  (2024-08-28T08:53:20Z)
• Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback [5.770351255180495]
  Image Retrieval with Relevance Feedback (IRRF) involves iterative human interaction during the retrieval process. We propose a new scheme based on a hyper-network, that is tailored to the task and facilitates swift adjustment to user feedback. We show that our method can attain SoTA results in few-shot one-class classification and reach comparable results in binary classification task of few-shot open-set recognition.
  arXiv  Detail & Related papers  (2023-12-18T10:20:28Z)
• RAFIC: Retrieval-Augmented Few-shot Image Classification [0.0]
  Few-shot image classification is the task of classifying unseen images to one of N mutually exclusive classes. We have developed a method for augmenting the set of K with an addition set of A retrieved images. We demonstrate that RAFIC markedly improves performance of few-shot image classification across two challenging datasets.
  arXiv  Detail & Related papers  (2023-12-11T22:28:51Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval [84.11127588805138]
  Composed Image Retrieval (CIR) combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. We propose Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training.
  arXiv  Detail & Related papers  (2023-02-06T19:40:04Z)
• Self Supervised Learning for Few Shot Hyperspectral Image Classification [57.2348804884321]
  We propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels.
  arXiv  Detail & Related papers  (2022-06-24T07:21:53Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification [10.80252725670625]
  In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classification. We show that only 35% of the predicted labels of a deep learning model's snapshots near its convergence were exactly the same. Using the snapshots committee to give out the informativeness of unlabeled data, the proposed AEDL achieved better performance on two real PolSAR images compared with standard active learning strategies.
  arXiv  Detail & Related papers  (2020-06-29T01:40:54Z)
• SAR2SAR: a semi-supervised despeckling algorithm for SAR images [3.9490074068698]
  Deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. Results on real images are discussed, to show the potential of the proposed algorithm.
  arXiv  Detail & Related papers  (2020-06-26T15:07:28Z)
• SCAN: Learning to Classify Images without Labels [73.69513783788622]
  We advocate a two-step approach where feature learning and clustering are decoupled. A self-supervised task from representation learning is employed to obtain semantically meaningful features. We obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime.
  arXiv  Detail & Related papers  (2020-05-25T18:12:33Z)
• Learning to Compare Relation: Semantic Alignment for Few-Shot Learning [48.463122399494175]
  We present a novel semantic alignment model to compare relations, which is robust to content misalignment. We conduct extensive experiments on several few-shot learning datasets.
  arXiv  Detail & Related papers  (2020-02-29T08:37:02Z)

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.