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In-domain representation learning for remote sensing [article]

Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby
2019 arXiv   pre-print
Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote  ...  sensing representation learning.  ...  REPRESENTATION/TRANSFER LEARNING IN REMOTE SENSING Since labeling remote sensing data is expensive, for a long time there was no equivalent to ImageNet and most benchmark datasets were small.  ... 
arXiv:1911.06721v1 fatcat:4izoowzbcfcrdeuppqbjyw7llu

Training general representations for remote sensing using in-domain knowledge [article]

Maxim Neumann, André Susano Pinto, Xiaohua Zhai, Neil Houlsby
2020 arXiv   pre-print
This paper investigates development of generic remote sensing representations, and explores which characteristics are important for a dataset to be a good source for representation learning.  ...  For this analysis, five diverse remote sensing datasets are selected and used for both, disjoint upstream representation learning and downstream model training and evaluation.  ...  In this paper we explore representation learning for remote sensing, and in particular in how much in-domain knowledge from related datasets could help in representation learning.  ... 
arXiv:2010.00332v1 fatcat:woi2j23t3jeurokgff2h3ydiv4

Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations [article]

Sachith Seneviratne, Kerry A. Nice, Jasper S. Wijnands, Mark Stevenson, Jason Thompson
2022 arXiv   pre-print
However, the performance of such methods on remote sensing imagery domains remains under-explored.  ...  In this work, we explore contrastive representation learning methods on the task of imagery-based city classification, an important problem in urban computing.  ...  learning techniques in the domains of remote sensing and abstract imagery.  ... 
arXiv:2203.04445v1 fatcat:ae2yu7dasvbjxfrteay4vnloju

Deep Learning for Remote Sensing Image Understanding

Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, Xue Cheng Tai
2016 Journal of Sensors  
the types and domains of remote sensing images.  ...  For different kinds of remote sensing images (e.g., SAR images and hyperspectral images), the corresponding specific feature representations are available.  ...  We appreciate all the authors for their submissions, as well as all the reviewers for their careful and professional review.  ... 
doi:10.1155/2016/7954154 fatcat:yoyzkgdi25er5hqggp4qub7plu

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.  ...  The rapid development of deep learning brings effective solutions for remote sensing image interpretation.  ...  By making the model focus on learning the shared feature representation in the source domain, the need for labeled training samples in the target domain can be reduced.  ... 
doi:10.1109/jstars.2021.3052869 fatcat:ldos3sx6mvaapjkgsua73l7tve

Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos

Wentao Yu, Jing Bai, Licheng Jiao
2020 IEEE Access  
The application of deep learning techniques in background subtraction for VHR optical remote sensing videos holds the potential to facilitate multiple intelligent remote sensing processing tasks.  ...  In our article, we design a novel deep learning network via fully utilizing GAN and domain adaptation, which has the ability to measure and minimize the discrepancy between feature distributions of natural  ...  In DANN-BS, the feature extractor is aimed to learn a hidden representation either for source frames or target frames so that the domain classifier is able to classify source domain inputs and target domain  ... 
doi:10.1109/access.2020.3004495 fatcat:hban6wuoxvdi5dkypk6yfxnuve

Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding [article]

Vladan Stojnić
2021 arXiv   pre-print
In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning.  ...  In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification.  ...  Self-supervised learning in remote sensing Lately, some research has been done in the area of applying self-supervised learning to the analysis of remote sensing images.  ... 
arXiv:2104.07070v2 fatcat:irqhsya535galjojxuy3dn57ci

The Role of Pre-Training in High-Resolution Remote Sensing Scene Classification [article]

Vladimir Risojević, Vladan Stojnić
2021 arXiv   pre-print
Finally, we show that in many cases the best representations are obtained by using a second round of pre-training using in-domain data, i.e. domain-adaptive pre-training.  ...  Due to the scarcity of labeled data, using models pre-trained on ImageNet is a de facto standard in remote sensing scene classification.  ...  INTRODUCTION Transfer learning opened up the possibility of applying deep learning to domains in which labeled data is scarce, difficult or expensive to acquire, such as remote sensing [1] .  ... 
arXiv:2111.03690v2 fatcat:x22h4s7mlvcllguyvv5czjwzzq

Remote Sensing Images Semantic Segmentation with General Remote Sensing Vision Model via a Self-Supervised Contrastive Learning Method [article]

Haifeng Li, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, Chao Tao
2021 arXiv   pre-print
Therefore, we propose Global style and Local matching Contrastive Learning Network (GLCNet) for remote sensing semantic segmentation.  ...  Our study promotes the development of self-supervised learning in the field of remote sensing semantic segmentation. The source code is available at https://github.com/GeoX-Lab/G-RSIM.  ...  However, there is a balance between global feature learning and local feature learning for remote sensing semantic segmentation tasks: from the perspective of global representations, remote sensing images  ... 
arXiv:2106.10605v1 fatcat:eohfunmdrzdflmhkupdda4wyry

State-of-the-art and gaps for deep learning on limited training data in remote sensing [article]

John E. Ball, Derek T. Anderson, Pan Wei
2018 arXiv   pre-print
The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain.  ...  Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled.  ...  target domains, and v) how does transfer learning work in the context of multisensor fusion for remote sensing?  ... 
arXiv:1807.11573v1 fatcat:q6vtrod6nvgtrihjafo25iz3wi

Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples [article]

Chao Tao, Ji Qi, Weipeng Lu, Hao Wang, Haifeng Li
2020 arXiv   pre-print
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification.  ...  The insights distilled from our studies can help to foster the development of SSL in the remote sensing community.  ...  The contributions of this work are mainly in two aspects: 1) We demonstrate that SSL is an entirely new paradigm which learns feature from unlabeled massive images for remote sensing image understanding  ... 
arXiv:2010.00882v1 fatcat:xqxmrld7p5avpng67hvbsrraly

Multi-adversarial Partial Transfer Learning with Object-level Attention Mechanism for Unsupervised Remote Sensing Scene Classification

Peng Li, Dezheng Zhang, Peng Chen, Xin Liu, Aziguli Wulamu
2020 IEEE Access  
In recent years, deep learning methods have been widely applied in remote sensing image classification tasks, providing valuable information for natural monitoring and spatial planning.  ...  INDEX TERMS Partial transfer learning, domain adaption, object-level attention, remote sensing scene classification, multi-adversarial learning, convolutional neural networks. 56650 This work is licensed  ...  Classical methods adopt sparse or low-rank representations [29] , [30] for image patches [31] and they are very effective in remote sensing area.  ... 
doi:10.1109/access.2020.2982034 fatcat:3exnuyctpvdzjgpj6alwmk7soi

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data [article]

Oscar Mañas, Alexandre Lacoste, Xavier Giro-i-Nieto, David Vazquez, Pau Rodriguez
2021 arXiv   pre-print
In this work, we propose Seasonal Contrast (SeCo), an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations.  ...  Second, a self-supervised algorithm that takes advantage of time and position invariance to learn transferable representations for remote sensing applications.  ...  Our goal is to exploit the massive amount of publicly available remote sensing data for learning good visual representations in a truly unsupervised way.  ... 
arXiv:2103.16607v2 fatcat:r7dukovfavckfbl4blkwqsdsiy

RESEARCH ON SE-INCEPTION IN HIGH-RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION

Z. L. Cai, Q. Weng, S. Z. Ye
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, a remote sensing image scene classification model based on SENet and Inception-V3 is proposed by utilizing the deep learning method and transfer learning strategy.  ...  With the deepening research and cross-fusion in the modern remote sensing image area, the classification of high spatial resolution remote sensing images has captured the attention of the researchers in  ...  ACKNOWLEDGEMENTS The study is supported by the National Natural Science Foundation of China (No. 41801324) and the Fujian Natural The International Archives of the Photogrammetry, Remote Sensing and Spatial  ... 
doi:10.5194/isprs-archives-xlii-3-w10-539-2020 fatcat:4qqlien54bg77jfwerwrxdfslm

Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images [article]

Pallavi Jain, Bianca Schoen-Phelan, Robert Ross
2022 arXiv   pre-print
This work proposes RSDnet, which applies the distillation network (BYOL) in the remote sensing (RS) domain where data is non-trivially different from natural RGB images.  ...  Self-Supervised learning (SSL) has become the new state-of-art in several domain classification and segmentation tasks. Of these, one popular category in SSL is distillation networks such as BYOL.  ...  in other domains such as remote sensing.  ... 
arXiv:2205.02049v1 fatcat:vvvdfxlucjhptew65zjxhpv6jy
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