# Semantic Deep Intermodal Feature Transfer: Transferring Feature Descriptors Between Imaging Modalities

## TL;DR

This paper presents Se-DIFT, a deep learning method for transferring feature descriptors between RGB and thermal images, enabling better intermodal localization under challenging environmental conditions.

## Contribution

It introduces a novel deep encoder-decoder architecture with a global feature vector to improve intermodal feature transfer between RGB and thermal images.

## Key findings

- Reduced L1 prediction error by over 7% compared to traditional U-Net.
- Effective transferability of SIFT, SURF, and ORB features across modalities.
- Enhanced intermodal feature matching accuracy.

## Abstract

Under difficult environmental conditions, the view of RGB cameras may be restricted by fog, dust or difficult lighting situations. Because thermal cameras visualize thermal radiation, they are not subject to the same limitations as RGB cameras. However, because RGB and thermal imaging differ significantly in appearance, common, state-of-the-art feature descriptors are unsuitable for intermodal feature matching between these imaging modalities. As a consequence, visual maps created with an RGB camera can currently not be used for localization using a thermal camera. In this paper, we introduce the Semantic Deep Intermodal Feature Transfer (Se-DIFT), an approach for transferring image feature descriptors from the visual to the thermal spectrum and vice versa. For this purpose, we predict potential feature appearance in varying imaging modalities using a deep convolutional encoder-decoder architecture in combination with a global feature vector. Since the representation of a thermal image is not only affected by features which can be extracted from an RGB image, we introduce the global feature vector which augments the auto encoder's coding. The global feature vector contains additional information about the thermal history of a scene which is automatically extracted from external data sources. By augmenting the encoder's coding, we decrease the L1 error of the prediction by more than 7% compared to the prediction of a traditional U-Net architecture. To evaluate our approach, we match image feature descriptors detected in RGB and thermal images using Se-DIFT. Subsequently, we make a competitive comparison on the intermodal transferability of SIFT, SURF, and ORB features using our approach.