Spatiotemporal Fusion in Remote Sensing
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Abstract (arXiv)
Remote sensing images and techniques are powerful tools to investigate earth surface. Data quality is the key to enhance remote sensing applications and obtaining a clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor, and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.
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7 of 101 references.
References · 7
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
- 2017Deep learning in remote sensing: a reviewcited by 128
- 2016Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networkscited by 25
- 2014Hyperspectral and Multispectral Image Fusion based on a Sparse Representationcited by 23
- 2017Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Networkcited by 14
- 20213D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Setscited by 3
- 2016Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutionscited by 1
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