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Advances in semi-supervised classification of hyperspectral remote sensing images

  • role： First author第一作者
  • Affiliation：

    Hunan University, Electrical and Information Engineering, Changsha 410080, China

    Pengcheng Laboratory , Shenzhen 518055,China

  • Email：hnuyangx@hnu.edu.cn
  • Introduction：杨星，研究方向为高光谱遥感图像分析。E-mail： hnuyangx@hnu.edu.cn
YANG Xing12,  
  • Affiliation：

    Hunan University, Electrical and Information Engineering, Changsha 410080, China

    Pengcheng Laboratory , Shenzhen 518055,China

FANG Leyuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Central South University, College of Automation, Changsha 410083, China

  • Email：junyue@csu.edu.cn
  • Introduction：岳俊，研究方向为遥感图像处理。E-mail：junyue@csu.edu.cn
YUE Jun3*

résumé

Hyperspectral remote sensing technology has been widely used in remote sensing, agriculture, geological exploration, and other fields, and hyperspectral image classification is one of the most important research directions. Benefiting from sufficient label information, supervised learning has achieved good results in this field. However, in many practical applications of hyperspectral remote sensing images, sufficient label samples are difficult to obtain. One of the most important reasons is the widespread use of hyperspectral remote sensing technology, which produces huge amounts of unlabeled data. Another is the high cost of labeling. Meanwhile, unsupervised learning cannot accurately cluster unknown data, and its clustering categories are to match to real categories. Both supervised and unsupervised learning have their unavoidable disadvantages. Therefore, semi-supervised learning that uses a large number of unlabeled samples and a small number of labeled samples should be explored. In recent years, significant progress has been made in the semi supervised classification of hyperspectral remote sensing images. Researchers have proposed many innovative algorithms and technologies to address the problem of insufficient data annotation. This article reviews the progress of the semi supervised classification research on hyperspectral remote sensing images in recent years, discussing key technologies and methods.This paper starts with semi-supervised classification and hyperspectral remote sensing technologies. First, the first part of this paper introduces some basic concepts of semi-supervised learning, including semi-supervised and unsupervised learning, supervised learning, and the application of semi-supervised learning. The second part introduces the development of hyperspectral remote sensing imaging technology domestically and internationally and the application of hyperspectral remote sensing in various fields, such as land and resource surveys, agriculture and forestry remote sensing, and urban environmental monitoring. Second, the three basic assumptions of the theory, process, and data distribution of semi-supervised learning are analyzed, and four typical types are introduced: low-density separation, generative, disagreement-based (difference-based), and graph-based methods. The algorithm flow and core ideas of each method are introduced in detail. The summarized current development status, typical algorithms, and research progress of hyperspectral remote sensing image classification are analyzed. Further, the advantages and disadvantages of each algorithm are enumerated. Then, common open-source algorithms were compared on three publicly available datasets, namely, Indian Pines, Pavia University, and Houston 2013. Finally, by analyzing existing semi-supervised learning technologies and experimental results, the challenging problems and development trends of semi-supervised learning in hyperspectral remote sensing are summarized.The graph-based semi-supervised classification method performs better than other semi-supervised classification methods, which may be because the graph model can model the relationship and similarity between samples, connect similar samples, and capture the intrinsic structure and similarity in a dataset.Semi-supervised learning can efficiently utilize both labeled data and unlabeled data. The future development trend of semi-supervised classification is mainly in three aspects: how to effectively use a large number of unlabeled samples; how to fully consider multiple factors, such as performance and computational complexity; and how to select features. These aspects will affect the stability, generalization, practicability, and performance of the algorithm.

mots-clés

hyperspectral image;semi-supervised classification;Low-density separation;generative model;graph neural network

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