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收稿日期：2025-02-03，
修回日期：2025-03-14，
录用日期：2025-03-24，
网络出版日期：2025-03-26，
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本论文结合国内外发展动态和团队三十余年高光谱图像分类研究实践，深入探讨、综述了高光谱图像分类的研究进展与未来发展趋势。从新的视角将多光谱和高光谱图像分类方法划分为四类：1. 传统方法，即特征提取加常规分类器的方法；2. 常规学习方法，即特征提取加常规学习分类器的方法；3. 深度学习方法，即基于深度学习的端对端自动特征挖掘与分类的方法；4. 数据与知识融合驱动的方法，即深度学习方法与领域知识和特征融合的方法。其中，第2至第4类方法统称为智能分类方法，是本文的主题。本文是国内外迄今第一篇高光谱图像智能分类研究综述论文。论文首先回顾并梳理了高光谱图像分类的背景和发展历程，介绍了为高光谱图像分类研究和验证测试提供基础的代表性高光谱卫星和高光谱数据集。接着，重点围绕特征挖掘和分类器两个核心方向，分别介绍了高光谱图像特征挖掘、传统分类方法、常规学习分类方法和深度学习分类方法，列举了若干代表性模型、方法及其应用案例。最后，讨论了该领域目前仍存在的问题和挑战，并对未来发展方向进行了讨论：数据与知识联合驱动的深度学习方法成为热点，多尺度、多分辨率、多特征、多分类器的有效融合是提高高光谱图像分类精度的重要途径，小样本学习、零样本迁移学习以及轻量化、有限精度神经网络在星载高光谱图像应用值得重视。研究表明：本文对高光谱图像分类方法的四类划分体现了技术的发展历史、当前重点和未来趋势，其中数据与知识融合的高光谱图像分类（即第4类方法）是对高光谱图像分类前沿研究方向的洞见，对未来研究和应用具有重要指导意义。
Hyperspectral imaging technology， emerging from the intricate integration of digital imaging and advanced spectral separation techniques， represents a remarkable leap in the field of remote sensing and data acquisition. This technology empowers the acquisition of images across an extensive range of multiple continuous narrow spectral channels， thereby constructing a three-dimensional data cube. This cube seamlessly amalgamates spatial and spectral information， giving rise to what is known as a hyperspectral image （HSI）. Hyperspectral images （HSIs） are not merely repositories of rich spectral information； they also comprehensively integrate spatial and radiometric information. This unique combination furnishes robust and indispensable support for the intricate classification， precise identification， and in-depth analysis of complex targets， which are often encountered in diverse real-world scenarios. Consequently， HSIs have been recognized to possess substantial application value across a plethora of civilian and military domains. In the aviation and aerospace sectors， they play a pivotal role in tasks such as terrain mapping， environmental monitoring from high altitudes， and the detection of potential hazards. In biomedical research， hyperspectral imaging is being increasingly utilized for non - invasive disease diagnosis， tissue characterization， and the study of physiological processes at a cellular level. In mineral exploration， it enables the identification of various minerals and the assessment of their abundance based on their distinct spectral signatures. In precision agriculture， HSIs are employed to monitor crop health， nutrient deficiencies， and water stress， thus optimizing agricultural practices for higher yields.Presently， hyperspectral imaging and processing technologies are still internationally regarded as highly promising and transformative areas of development. However， HSIs are characterized by deeply coupling and diverse forms of information redundancies across spatial， spectral， and temporal domains. These inherent characteristics pose unique and formidable challenges， most notably the "curse of dimensionality" and the issue of mixed pixels. The "curse of dimensionality" refers to the exponential increase in computational complexity and data volume with the rise in the number of spectral bands， which often leads to overfitting and a significant degradation in classification performance. Mixed pixels， on the other hand， occur when a single pixel in the image encompasses multiple land cover types， making it extremely difficult to accurately classify the pixel's true nature. These issues render traditional pattern recognition and digital image processing methods ill-equipped for direct application to HSI classification. Despite the remarkable progress achieved in multispectral and hyperspectral imaging and their associated processing techniques， numerous challenges persist， and their full potential remains to be fully harnessed.Drawing on the latest global developments and the team's over three-decade-long research practice， this paper undertakes an in-depth exploration and comprehensive review of the research progress and future trends in HSI classification from a novel perspective. It systematically categorizes multispectral and HSI classification methods into four distinct types：（1） Traditional methods， which typically involve a two-step process of feature extraction followed by the application of conventional classifiers such as maximum likelihood classifiers. These methods rely on hand-crafted features that are designed based on prior knowledge and assumptions about the data.（2） Conventional learning methods， which integrate feature extraction with conventional learning-based classifiers. These classifiers， such as support vector machines and multilayper perceptrons， are capable of learning from the data to some extent， but still require manual intervention in feature mining.（3） Deep learning methods， which leverage the power of neural networks for automated feature extraction and classification. These methods， such as convolutional neural networks and recurrent neural networks， can automatically learn hierarchical features from the data without the need for explicit feature mining.（4） Data and knowledge fusion methods， which combine deep learning with knowledge-based and feature-fusion techniques. These methods aim to incorporate prior knowledge and multiple sources of information into the deep-learning framework to enhance the classification performance. Among these， methods 2 to 4 are collectively denoted as intelligent classification methods and constitute the focal point of this paper. As far as we know， this article is the first review paper in the world that specifically and systematically overview the intelligent classification of hyperspectral images.The paper commences with a detailed review of the background and development history of HSI classification， tracing its evolution from the early days of remote sensing to the current state-of-the-art techniques. It also provides an in-depth introduction to representative hyperspectral satellites and datasets， which serve as the bedrock for research and validation in this field. Subsequently， it accentuates two core directions—feature mining （including feature extraction， feature or band selection， and feature combination） and classifiers—and delves into the discussion of HSI feature mining， traditional classification methods， conventional learning classification methods， and deep learning classification methods. For each category， it presents examples of representative models， methods， and their real-world applications. Finally， it confronts the existing challenges and problems in the field and probes into the future directions. The effective fusion of multi-scale， multi-resolution， multi-feature， and multi-classifier approaches is identified as a crucial avenue for enhancing the accuracy of HSI classification. Additionally， small-sample learning， zero-shot transfer learning， and lightweight， limited-precision neural networks tailored for onboard HSI applications merit close attention.Research findings indicate that the four-category classification of HSI methods proposed in this paper accurately mirrors the historical development， current research focus， and future trends of the technology. Among these， data and knowledge fusion-based classification （i.e.， the fourth category） offers profound insights into the cutting-edge research of HSI classification and holds substantial guiding value for future studies and practical applications.
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