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Few-Shot Ship Classification in Optical Remote Sensing Images Using Nearest Neighbor Prototype Representation

Jiawei Shi, Zhiguo Jiang, Haopeng Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
With the development of ship detection in optical remote sensing images, it is convenient to obtain accurate detection results and ship images.  ...  Different from image-to-image measure in common few-shot methods, we use an image-to-feature measure.  ...  INTRODUCTION G REAT progress in remote sensing has been made in recent years, which makes it more convenient to get high-resolution and high-quality optical remote sensing images (RSIs).  ... 
doi:10.1109/jstars.2021.3066539 fatcat:ayuppzolhvbgxkssas7vu6vhau

Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector

Haopeng Zhang, Xingyu Zhang, Gang Meng, Chen Guo, Zhiguo Jiang
2022 Remote Sensing  
To solve this problem, in this paper, we propose a few-shot multi-class ship detection algorithm with attention feature map and multi-relation detector (AFMR) for remote sensing images.  ...  Monitoring and identification of ships in remote sensing images is of great significance for port management, marine traffic, marine security, etc.  ...  [17] proposed a few-shot ship classification method based on prototype representation, Ref. [18] proposed a meta-learning model for object detection in remote sensing images. Ref.  ... 
doi:10.3390/rs14122790 fatcat:k74tq5tgvjhbrk26gmamotuh5q

Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images [article]

Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui, Hanene Azzag
2021 arXiv   pre-print
This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images.  ...  It highlights in particular some intrinsic weaknesses for the few-shot object detection task. Finally, some suggestions and perspectives are formulated according to these insights.  ...  Acknowledgment The authors would like to thank COSE for their close collaboration and the funding of this project.  ... 
arXiv:2109.13027v1 fatcat:7olfib6l2bhtzdjp7pmp4vr2li

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

Qingjie Zeng, Jie Geng, Kai Huang, Wen Jiang, Jun Guo
2021 Remote Sensing  
The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training.  ...  To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification.  ...  Few-shot learning has been developed for many computer vision tasks, including image classification [34, 35] , object detection, segmentation [36] , and so on.  ... 
doi:10.3390/rs13142728 fatcat:ibt2tqwitnc6lduggwoctaaq2u

Few Shot Object Detection for SAR Images via Feature Enhancement and Dynamic Relationship Modeling

Shiqi Chen, Jun Zhang, Ronghui Zhan, Rongqiang Zhu, Wei Wang
2022 Remote Sensing  
Comprehensive experiments have been completed on the self-constructed SAR multi-class object detection dataset, which demonstrate the effectiveness of our few-shot object detection framework in learning  ...  To tackle this problem, a novel few-shot SAR object detection framework is proposed, which is built upon the meta-learning architecture and aims at detecting objects of unseen classes given only a few  ...  For few-shot-classification methods in optical remote sensing images, Shi et al. [26] presented a metric-based few-shot method to generate prototypes for novel classes.  ... 
doi:10.3390/rs14153669 fatcat:kwmlenb3ivbjzniz4tchwt75wa

Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation

Xian Sun, Bing Wang, Zhirui Wang, Hao Li, Hengchao Li, Kun Fu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This article gives a reference for scholars working on few-shot learning research in the remote sensing field.  ...  Therefore, it is of great significance to conduct the research on few-shot learning for remote sensing image interpretation.  ...  At the present stage, the task of few-shot object detection for remote sensing images is still in its infancy, and there is a lack of public available remote sensing datasets for supporting the relevant  ... 
doi:10.1109/jstars.2021.3052869 fatcat:ldos3sx6mvaapjkgsua73l7tve

SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification

Joseph Kim, Mingmin Chi
2021 Remote Sensing  
refining network and global pooling operation for a few-shot remote sensing classification task.  ...  The proposed model is evaluated on three publicly available datasets for few shot remote sensing scene classification.  ...  ) remote sensing images.  ... 
doi:10.3390/rs13132532 fatcat:fonmvysiczct5kr7wwl3xe2xdm

Few-Shot Learning for Post-Earthquake Urban Damage Detection

Eftychia Koukouraki, Leonardo Vanneschi, Marco Painho
2021 Remote Sensing  
Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address  ...  The use of remotely sensed imagery is popular in damage assessment applications; however, it requires a considerable amount of labeled data, which are not always easy to obtain.  ...  Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS J. Photogramm. Remote Sens. 2020, 159, 296–307.  ... 
doi:10.3390/rs14010040 fatcat:jeombwrohzfjpiserludgp2lti

TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification

Wendong Huang, Zhengwu Yuan, Aixia Yang, Chan Tang, Xiaobo Luo
2021 Remote Sensing  
In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples.  ...  For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net.  ...  Data Availability Statement: The data presented in this study are available from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14010111 fatcat:6qjuedqtabhzro67e7tsikbweq

Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification

Jiayan Wang, Xueqin Wang, Lei Xing, Bao-Di Liu, Zongmin Li
2022 Remote Sensing  
Then, we propose a novel classifier for the few-shot remote sensing scene classification named Class-Shared SparsePCA classifier (CSSPCA).  ...  We propose a novel method for few-shot remote sensing scene classification based on shared class Sparse Principal Component Analysis (SparsePCA) to solve this problem.  ...  Acknowledgments: We would like to express our gratitude to the editor and reviewers for their valuable comments.  ... 
doi:10.3390/rs14102304 fatcat:meounr3krjcwngryw5jujxbgpq

Weakly Supervised Few-Shot Segmentation Via Meta-Learning [article]

Pedro H. T. Gama, Hugo Oliveira, José Marcato Junior, Jefersson A. dos Santos
2021 arXiv   pre-print
In this paper, we present two novel meta learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations.  ...  We conducted extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are very distinct fields of knowledge and usually  ...  Few-shot Semantic Segmentation: Sparse vs Dense labels In this section, we present the results of our methods in multiple few-shot tasks in the Medical and Remote Sensing scenarios.  ... 
arXiv:2109.01693v1 fatcat:ur43ebrgivhghctunkvkequkky

AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification

Xirong Li, Fangling Pu, Rui Yang, Rong Gui, Xin Xu
2020 Remote Sensing  
Experiments on the NWPU-RESISC45 dataset and the RSD-WHU46 dataset demonstrate that our method achieves the state-of-the-art results on one-shot remote sensing image scene classification tasks.  ...  In this paper, we propose an attention metric network (AMN) in the framework of the few-shot learning (FSL) to improve the performance of one-shot scene classification.  ...  In the field of remote sensing, saliency detection is usually adopted to obtain regions of interest. For example, Zhang et al.  ... 
doi:10.3390/rs12244046 fatcat:akydgcq7rnhttgxao7gqmfgbue

Few-Shot Scene Classification with Multi-Attention DeepEMD Network in Remote Sensing

Zhengwu Yuan, Wendong Huang, Lin Li, Xiaobo Luo
2020 IEEE Access  
in few-shot remote sensing scene classification.  ...  For this reason, an efficient few-shot scene classification scheme in remote sensing is proposed by combining multiple attention mechanisms and the attention-reference mechanism into the deepEMD network  ...  ResNet-18 is carefully selected as the backbone for extracting remote sensing scene image features in this paper.  ... 
doi:10.1109/access.2020.3044192 fatcat:2aej4u6ivzet3hrizazye3anga

An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation

Xiangkai Xu, Zhejun Feng, Changqing Cao, Mengyuan Li, Jin Wu, Zengyan Wu, Yajie Shang, Shubing Ye
2021 Remote Sensing  
A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images.  ...  Remote sensing image object detection and instance segmentation are widely valued research fields.  ...  Acknowledgments: The authors thank the team of optical sensing and measurement of Xidian University for their help.  ... 
doi:10.3390/rs13234779 fatcat:qclznhggczcojcc5okxgypyqfm

A Review of Deep Learning in Multiscale Agricultural Sensing

Dashuai Wang, Wujing Cao, Fan Zhang, Zhuolin Li, Sheng Xu, Xinyu Wu
2022 Remote Sensing  
Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales  ...  In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra.  ...  remote sensing.  ... 
doi:10.3390/rs14030559 fatcat:fcgpljr2tfhpjd3nvmi3kgp3bq
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