Spatio-temporal fusion methods for spectral remote sensing: a comprehensive technical review and comparative analysis

Tropical Ecology - Trang 1-20 - 2023
Ratnakar Swain1, Ananya Paul2, Mukunda Dev Behera3
1Department of Civil Engineering, National Institute of Technology (NIT), Rourkela, India
2Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, India
3Department of CORAL, Indian Institute of Technology Kharagpur, Kharagpur, India

Tóm tắt

For many years, spectral remote sensing has been essential for research on the Earth’s surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion’s difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community.

Tài liệu tham khảo

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