# Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

arXiv: https://arxiv.org/abs/1807.09954 · HTML: https://mlsanity.com/paper/1807.09954/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series](https://mlsanity.com/paper/2112.07558/) (2021)
- [ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification](https://mlsanity.com/paper/2412.02044/) (2024)
- [Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.15770/) (2024)
- [Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification](https://mlsanity.com/paper/2106.11056/) (2021)
- [An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images](https://mlsanity.com/paper/2209.08984/) (2022)
- [Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model](https://mlsanity.com/paper/2102.06260/) (2021)
- [An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images](https://mlsanity.com/paper/2109.00400/) (2021)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)

## Abstract (arXiv)

In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SARoptical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image, meanwhile, the model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SARoptical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal superresolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.