计算机科学
合成孔径雷达
遥感
卫星
过程(计算)
地球观测
计算机视觉
人工智能
地质学
工程类
操作系统
航空航天工程
作者
Oscar Sommervold,Michele Gazzea,Reza Arghandeh
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2023-02-03
卷期号:15 (3): 850-850
被引量:36
摘要
After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge in SAR-optical matching for many years. Combining the two sensors will improve the quality of existing and future remote sensing applications across multiple industries. Several approaches have emerged as promising candidates in the search for combining SAR and optical imagery. In addition, recent research has indicated that machine learning-based approaches have great potential for filling the information gap posed by utilizing only one sensor type in Earth observation applications. However, several challenges remain, and combining them is a multi-step process where no one-size-fits-all approach is available. This article reviews traditional, state-of-the-art, and recent development trends in SAR-optical co-registration methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI