Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

TL;DR

This paper introduces a novel audiovisual approach to aerial scene recognition, leveraging sound event knowledge to enhance visual classification accuracy, supported by a new dataset and three transfer learning methods.

Contribution

It proposes a new multimodal framework that transfers sound event knowledge to improve aerial scene recognition, along with a new dataset for evaluation.

Findings

Audio information improves scene recognition accuracy

Three transfer methods demonstrate effective knowledge transfer

New dataset ADVANCE supports multimodal aerial scene analysis

Abstract

Aerial scene recognition is a fundamental task in remote sensing and has recently received increased interest. While the visual information from overhead images with powerful models and efficient algorithms yields considerable performance on scene recognition, it still suffers from the variation of ground objects, lighting conditions etc. Inspired by the multi-channel perception theory in cognition science, in this paper, for improving the performance on the aerial scene recognition, we explore a novel audiovisual aerial scene recognition task using both images and sounds as input. Based on an observation that some specific sound events are more likely to be heard at a given geographic location, we propose to exploit the knowledge from the sound events to improve the performance on the aerial scene recognition. For this purpose, we have constructed a new dataset named AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition. The source code is publicly available for reproducibility purposes.