pith. sign in

arxiv: 1807.01569 · v1 · pith:MJHI522Knew · submitted 2018-07-04 · 💻 cs.CV

The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion

classification 💻 cs.CV
keywords datadatasetdeepfusionlearningsen1-2imagesar-optical
0
0 comments X
read the original abstract

While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In particular, this holds for applications involving data from multiple sensors with heterogeneous characteristics. One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, we show exemplary results for several possible applications, such as SAR image colorization, SAR-optical image matching, and creation of artificial optical images from SAR input data. Since SEN1-2 is the first large open dataset of this kind, we believe it will support further developments in the field of deep learning for remote sensing as well as multi-sensor data fusion.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling

    cs.CV 2026-05 conditional novelty 7.0

    MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.

  2. SMART-Ship: A Comprehensive Synchronized Multi-modal Aligned Remote Sensing Targets Dataset and Benchmark for Berthed Ships Analysis

    cs.CV 2025-08 unverdicted novelty 7.0

    SMART-Ship introduces a new synchronized multi-modal remote sensing dataset with fine-grained annotations for berthed ships and benchmarks for five interpretation tasks.

  3. TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images

    cs.CV 2026-05 unverdicted novelty 6.0

    TAR uses frozen text encoders on remote sensing scene descriptions to boost high-level features for coarse-to-fine optical-SAR image registration under large deformations.