【摘要】 高光谱作为一种图谱合一的成像技术，在对地观测、航空航天领域具有十分重要的应用。然而，作为光学遥感的分支，高光谱成像易受到大气、光照等因素的影响。高光谱图像本征信息分解旨在抑制复杂环境因素对地物光谱与空间特征的影响，准确提取并表征观测场景最本征的光谱与空间信息，提升高光谱图像识别与解译性能。本文主要对代表性的高光谱图像本征信息分解的模型和方法进行综述，系统地分析了各种典型方法的原理及优缺点，进一步阐述了实际遥感应用中现有本征信息分解面临的挑战性难题，并结合遥感实际应用，对高光谱图像本征信息分解技术的发展趋势进行了展望。更多还原

【Abstract】 Hyperspectral imaging is a powerful image acquisition method which can record the rich spectral and spatial information of the scene in a high dimensional data cube. Due to this advantage, hyperspectral imaging has been very useful in many practical applications of earth observation and aerospace. However, as a branch of optical remote sensing, the performance of hyperspectral imaging may be affected by many factors such as atmosphere and illumination. The objective of hyperspectral intrinsic image decomposition is to decrease the influence of complex environmental factors, extract and represent the intrinsic spectral and spatial information of hyperspectral images accurately, so as to improve the performance of hyperspectral image recognition and interpretation. This paper reviews some representative work in hyperspectral intrinsic image decomposition. The principle, advantages, and disadvantages of some typical intrinsic image decomposition methods have been analyzed. Moreover, the challenging problems of intrinsic image decomposition faced in real remote sensing applications have been illustrated. At last, based on the requirements of practical remote sensing applications, we discuss the development trends of hyperspectral intrinsic image decomposition. This review could be a good guide for those researchers who are interested in the advances and applications of hyperspectral remote sensing. More importantly, it gives some important future research directions that could be investigated in the future.更多还原