A review for sample datasets of remote sensing imagery
- Vol. 26, Issue 4, Pages: 589-605(2022)
Published： 07 April 2022
DOI： 10.11834/jrs.20221162
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摘要
随着机器学习、深度学习等人工智能技术在遥感领域的不断应用与发展，基于海量样本的数据驱动模型已经成为遥感影像信息提取的一种新的研究范式，其对样本数据的规模、质量、多样性等提出了更高要求。最近，国内外众多学者和研究机构相继发布了一系列遥感影像样本数据集，为大数据时代下遥感影像的信息提取和智能解译等奠定了研究基础。然而目前尚缺乏对上述影像样本数据集的综合分析，针对这一问题，本文在文献检索与分析的基础上，归纳总结了124个具有一定影响力且应用广泛的遥感影像样本数据集并对其元数据进行了分析，并提供了数据来源、应用领域与关键词的发展变化，分析了数据集在空间、时间、光谱分辨率上的差异，以应用领域为依据将其划分为场景识别、土地覆被/利用分类、专题要素提取、变化检测、目标检测、语义分割等8个类别并以部分数据为例进行了具体分析，总结了深度学习模型在数据集上的研究进展，并针对稀疏样本导致的模型过拟合问题，探讨了样本时空迁移、小样本和零样本学习、样本主动发现、样本生成等在遥感影像信息提取中的应用前景。本文首次对遥感影像样本数据集进行了综述研究，可为相关领域科研人员提供数据参考。
Abstract
With the rapid development of artificial intelligence technology such as machine learning and deep learning in remote sensing
data-driven models have become a new research paradigm for automatic information retrieval from remote sensing imagery
calling for higher requirements for the quantity
quality
and diversity of sample datasets. Before the era of deep learning
because classical machine learning methods (e.g.
support vector machine and random forest) do not require huge numbers of samples for model training
the previously published sample datasets usually have a relatively small size (i.e.
less than 100). In recent years
with the rapid development of technologies such as big data
parallel computing
and deep learning
many scholars and research institutions have issued a series of sample datasets
laying a solid foundation for a wide range of research and applications such as scene understanding
semantic segmentation
and object detection from remote sensing images. However
comprehensive review of the recently published sample datasets for remote sensing image analysis under the context of big data and deep learning remains lacking. Therefore
the objective of this study is to summarize and analyze these datasets to provide a valuable data reference for relevant researchers.
On the basis of literature retrieval and analysis
this paper summarized a total of 124 widely used
open access
and influential remote sensing image sample datasets that were published between 2001 and 2020.
We reviewed and summarized the development of recently published sample datasets for remote sensing imagery based on metadata analysis from the following aspects
such as data sources
application fields
keywords
and data size. Afterward
we analyzed these sample datasets from the perspective of spatial
spectral
and temporal resolutions. We listed the commonly used deep learning models (e.g.
convolutional neural networks
recurrent neural networks
and generative adversarial networks) in the remote sensing field to show how these sample datasets could be used. We also divided the remote sensing image sample datasets into eight categories based on the following application fields: scene recognition
land cover/land use classification
thematic information extraction
change detection
ground-object detection
semantic segmentation
quantitative remote sensing
and other applications. The typical datasets and related research progress were carefully reviewed for each application field. In addition
because deep learning models are data-hungry
how to train a model with good generalization capability under limited labeled data has become a significant issue
especially for remote sensing applications given that obtaining sufficient labeled samples is time-consuming. To address this issue
we discussed several methods that could increase the model’s generalization capability
including sample transfer between spatio-temporal domains
few-shot learning
and zero-shot learning
active learning
and semi-supervised learning for sample discovery
as well as sample generation through generative adversarial networks.
By means of multi-dimensional analysis
we give a comprehensive overview of remote sensing image sample datasets. To the best of our knowledge
this paper is the first review of remote sensing image sample datasets for deep learning
potentially providing data reference for researchers in related fields.
关键词
遥感影像样本数据集机器学习深度学习
Keywords
remote sensing imagerysample datasetsmachine learningdeep learning
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