Multimodal Object Detection in Remote Sensing

TL;DR

This paper reviews multimodal object detection in remote sensing, comparing methods, surveying datasets, and discussing future research directions to enhance detection accuracy using data fusion.

Contribution

It provides a comprehensive comparison of multimodal detection methods, surveys datasets, and outlines future research avenues in remote sensing object detection.

Findings

Multimodal data fusion improves detection accuracy.

Surveyed available datasets for multimodal remote sensing.

Identified key challenges and future directions.

Abstract

Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.