# Spatiotemporal Fusion in Remote Sensing

arXiv: https://arxiv.org/abs/2107.02701 · HTML: https://mlsanity.com/paper/2107.02701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 101 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks](https://mlsanity.com/paper/1609.06846/) (2016)
- [Hyperspectral and Multispectral Image Fusion based on a Sparse Representation](https://mlsanity.com/paper/1409.5729/) (2014)
- [Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network](https://mlsanity.com/paper/1706.05249/) (2017)
- [3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets](https://mlsanity.com/paper/2107.00590/) (2021)
- [Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions](https://mlsanity.com/paper/1606.07279/) (2016)

## 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.