高光谱地物要素识别潜力分析与前景展望

doi:10.11947/j.AGCS.2023.20220495

摘要/Abstract

摘要： 近年来，人工智能方法在高光谱遥感领域得到了广泛应用，特别是基于深度学习的影像分析和信息提取技术已成为持久的热点，有力地推动了地物光谱探测的精细化和智能化水平。本文在分析地物要素光谱探测潜力与需求的基础上，系统地介绍和总结了高光谱影像分析方面的进展，针对高光谱地物探测的智能化问题，重点讨论了近年来深度学习的新思路。首先，结合地形要素分类体系和高光谱探测能力，将高光谱地物要素划分为植被、土质、水域和人工建筑物4大类及若干子类，并分析4种地物要素的光谱响应特性和高光谱地物探测的优势。然后，在影像分析方面，重点梳理了波段选择、特征提取、模式分类和分类后处理等影像分析技术的研究进展，给出研究方向和热点；在智能化处理部分，按照监督学习、半监督学习及自监督学习的思路，系统总结了当前应用于高光谱地物探测的深层神经网络模型，同时分析了迁移学习、元学习等机器学习策略的研究情况。最后，结合上述分析，对高光谱影像地物探测的发展趋势加以展望，以期拓展下一步的研究思路。

关键词: 高光谱地物探测, 高光谱影像分析, 深度学习, 半监督学习, 自监督学习, 元学习, 迁移学习

Abstract: Hyperspectral remote sensing technology has fully released its potential in geospatial information acquisition due to the unique technical advantages in massive data analysis, information fusion, and recognition of ground objects. Due to the nonlinearity of hyperspectral data and complexity of ground objects, there are problems of data heterogeneity and sample scarcity when using hyperspectral images for land cover classification. Classical hyperspectral image analysis methods have the characteristics of a small amount of calculation, fewer labeled samples requirement, and strong theoretical interpretability, which play an important role in the extraction of ground objects attribute information. To cope with the rapid increase in the amount of data and more diverse applications, it is urgent to develop automatic and intelligent hyperspectral recognition technology for land cover classification. In recent years, artificial intelligence methods have been studied and widely applied in the field of hyperspectral remote sensing. In particular, image analysis and information extraction technology based on deep learning has become a persistent hot spot, which has effectively promoted the refinement and intelligence of hyperspectral recognition of ground objects. Based on a brief analysis of the potential and demand of recognition of ground objects, this article systematically summarizes several advancements in hyperspectral image analysis, and focuses on new ideas of deep learning in recent years for the intelligent recognition of hyperspectral ground objects. Firstly, combining the terrain elements and the capability of hyperspectral detection, the hyperspectral ground objects are divided into four categories (i.e., vegetation, soil, water, and artificial buildings) and several sub-categories, and spectral response characteristics of four ground objects are also analyzed; thereafter, in terms of image analysis research, the image analysis technologies especially band selection, feature extraction, pattern classification, and post-classification processing are reviewed, and new research directions and hotspots are also analyzed. In terms of intelligent processing, according to the schedule of supervised learning, semi-supervised learning, and self-supervised learning, the deep neural network models applied in hyperspectral ground object recognition are systematically summarized, and the applications of transfer learning as well as meta learning are also analyzed. Finally, based on the above analysis, the development trends of hyperspectral ground object recognition are also prospected, so as to expand the research ideas in the future.

Key words: hyperspectral ground object recognition, hyperspectral image analysis, deep learning, semi-supervised learning, self-supervised learning, meta learning, transfer learning

中图分类号:

P237

余旭初, 刘冰, 薛志祥. 高光谱地物要素识别潜力分析与前景展望[J]. 测绘学报, 2023, 52(7): 1115-1125.

YU Xuchu, LIU Bing, XUE Zhixiang. Potential analysis and prospect of hyperspectral ground object recognition[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1115-1125.

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