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Meta-Learning for Few-Shot Land Cover Classification [article]

Marc Rußwurm, Sherrie Wang, Marco Körner, David Lobell
2020 arXiv   pre-print
We evaluate the model-agnostic meta-learning (MAML) algorithm on classification and segmentation tasks using globally and regionally distributed datasets.  ...  We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on (1) the Sen12MS dataset and (2) DeepGlobe data when the source domain and target domain  ...  Discussion and Conclusion In this work, we evaluated the model-agnostic metalearning (MAML) algorithm for few-shot problems in land cover classification to adapt deep learning models to individual regions  ... 
arXiv:2004.13390v1 fatcat:s5bsq22j6rfg5lwamcia3jmbsu

Meta-Learning for Few-Shot Land Cover Classification

Marc Ruswurm, Sherrie Wang, Marco Korner, David Lobell
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We evaluate the modelagnostic meta-learning (MAML) algorithm on classification and segmentation tasks using globally and regionally distributed datasets.  ...  We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on the (1) Sen12MS dataset and (2) DeepGlobe dataset when the source domain and target domain  ...  Discussion and Conclusion In this work, we evaluated the model-agnostic metalearning (MAML) algorithm for few-shot problems in land cover classification to adapt deep learning models to individual regions  ... 
doi:10.1109/cvprw50498.2020.00108 dblp:conf/cvpr/RusswurmW0L20 fatcat:tiqxnwuhgnfdzow6ufgm2dwfcm

Land Cover Mapping in Limited Labels Scenario: A Survey [article]

Rahul Ghosh, Xiaowei Jia, Vipin Kumar
2021 arXiv   pre-print
Land cover mapping is essential for monitoring global environmental change and managing natural resources.  ...  In this survey, we provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems.  ...  ., 2020] proposed a novel meta learning framework for few-shot RS scene classification.  ... 
arXiv:2103.02429v2 fatcat:obltmtly6nfwzcbmkq6qlytp6i

Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification

Yong Li, Zhenfeng Shao, Xiao Huang, Bowen Cai, Song Peng
2021 Remote Sensing  
In this study, we propose Meta-FSEO, a novel model for improving the performance of few-shot remote sensing scene classification in varying urban scenes.  ...  These results indicated that the proposed Meta-FSEO model outperformed both the transfer learning-based algorithm and two popular meta-learning-based methods, i.e., MAML and Meta-SGD.  ...  Acknowledgments: The authors are sincerely grateful to Steve McClure for revised the grammatical errors in the paper, and the editors, as well as the anonymous reviewers, for their valuable suggestions  ... 
doi:10.3390/rs13142776 fatcat:mf32i6f2wrbnfmpkdda3j3m3ny

Few-Shot Classification of Aerial Scene Images via Meta-Learning

Pei Zhang, Yunpeng Bai, Dong Wang, Bendu Bai, Ying Li
2020 Remote Sensing  
To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs.  ...  In this work, we introduce few-shot learning to the aerial scene classification problem.  ...  Acknowledgments: We would like to express our gratitude to the editor and reviewers for their valuable comments.  ... 
doi:10.3390/rs13010108 fatcat:4vtnq3ceg5hcjfbtpkxu66mvzy

RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

Pei Zhang, Ying Li, Dong Wang, Jiyue Wang
2021 Sensors  
Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible.  ...  In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner.  ...  A well-known algorithm, Model-agnostic meta-learning (MAML) [42] , is evaluated for few-shot problems in land cover classification [47] .  ... 
doi:10.3390/s21051566 pmid:33668138 pmcid:PMC7956409 fatcat:x6znnh3lqfbz3jmvzckreh37km

Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification

Pei Zhang, Guoliang Fan, Chanyue Wu, Dong Wang, Ying Li
2021 Remote Sensing  
The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples.  ...  Here we propose a novel Task-Adaptive Embedding Learning (TAEL) framework that complements the existing methods by giving full play to feature embedding's dual roles in few-shot scene classification—representing  ...  [21] evaluate a well-known meta-learning algorithm, Model-Agnostic Meta-Learning (MAML) [19] , for land cover few-shot classification problems.  ... 
doi:10.3390/rs13214200 fatcat:ejvfwk7ofzb6tdrjuzlryejkeq

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.  ...  The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.  ...  Hence, few-shot learning is generally regarded as a special case of meta-learning.  ... 
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).  ...  A few-shot remote sensing scene classification framework contains two phases: (i) the pre-training phase seeks to adopt base data to train a feature extractor, and (ii) the meta-testing phase uses the  ...  Acknowledgments: We would like to express our gratitude to the editor and reviewers for their valuable comments.  ... 
doi:10.3390/rs14102304 fatcat:meounr3krjcwngryw5jujxbgpq

Semi-Supervised Few-Shot Learning with Prototypical Random Walks [article]

Ahmed Ayyad, Yuchen Li, Nassir Navab, Shadi Albarqouni, Mohamed Elhoseiny
2021 arXiv   pre-print
Our work is related to the very recent development of graph-based approaches for few-shot learning.  ...  Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL).  ...  is not reported for 1 and 5-shot classification.  ... 
arXiv:1903.02164v3 fatcat:oqhk37xdp5hblpxhgt3g6wbvhq

TIML: Task-Informed Meta-Learning for Agriculture [article]

Gabriel Tseng and Hannah Kerner and David Rolnick
2022 arXiv   pre-print
We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning  ...  While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling  ...  Tseng et al. (2021a) and Tseng et al. (2021b) used meta-learning for agricultural land cover classification.  ... 
arXiv:2202.02124v1 fatcat:3mtitlt6srcehiretruhug6fwu

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.  ...  In RS-MetaNet [21] , a meta-training strategy is developed to learn a generalized distribution for few-shot scene classification.  ... 
doi:10.3390/rs13142728 fatcat:ibt2tqwitnc6lduggwoctaaq2u

Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images

Zhengwu Yuan, Wendong Huang, Chan Tang, Aixia Yang, Xiaobo Luo
2022 Remote Sensing  
For this reason, in this paper, an end-to-end graph neural network is presented to enhance the performance of scene classification in few-shot scenarios, referred to as the graph-based embedding smoothing  ...  Existing methods seek to take advantage of transfer knowledge or meta-knowledge to resolve the scene classification issue of remote sensing images with a handful of labeled samples while ignoring various  ...  Few-Shot Learning At present, most popular few-shot learning algorithms adopt the meta-learning framework [32] [33] [34] [35] [36] .  ... 
doi:10.3390/rs14051161 fatcat:hvxzgzeg2bblvnpppdblgogd7y

Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification [article]

Qiaoling Chen, Zhihao Chen, Wei Luo
2022 arXiv   pre-print
Experiments on RSSC and land-cover mapping tasks verified its capability to handle cross-domain few-shot problems.  ...  Moreover, FTM can be effectively learned on target domain in the case of few training data available and is agnostic to specific network structures.  ...  GID provides two subsets -a large-scale classification set (Set-C) and a fine land-cover classification set (Set-F).  ... 
arXiv:2203.02270v1 fatcat:arrmqttedbffhlrb4eadzekjam

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.  ...  Additionally, other efficient CNN architectures will be searched by a neural architecture search [65] strategy for few-shot remote sensing land-cover/use image classification tasks.  ... 
doi:10.3390/rs13132532 fatcat:fonmvysiczct5kr7wwl3xe2xdm
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