# Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

arXiv: https://arxiv.org/abs/1801.08467 · HTML: https://mlsanity.com/paper/1801.08467/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 18 of 18)

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- [MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings](https://mlsanity.com/paper/2110.03302/) (2021)
- [Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification](https://mlsanity.com/paper/2205.01920/) (2022)
- [A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images](https://mlsanity.com/paper/2404.19311/) (2024)
- [Siamese Transformer Networks for Few-shot Image Classification](https://mlsanity.com/paper/2408.01427/) (2024)
- [Multi-Object Grounding via Hierarchical Contrastive Siamese Transformers](https://mlsanity.com/paper/2504.10048/) (2025)

## Abstract (arXiv)

In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure. Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching