Visual Question Answering on Multiple Remote Sensing Image Modalities

TL;DR

This paper introduces a new multi-modal VQA dataset and model for remote sensing images, leveraging diverse spectral, spatial, and contextual data to improve scene understanding and question answering accuracy.

Contribution

It presents a novel multi-modal VQA dataset (TAMMI) and a transformer-based model (MM-RSVQA) for remote sensing, enabling effective fusion of multiple image modalities.

Findings

Achieved 65.56% accuracy on the VQA task.

Demonstrated the feasibility of multi-modal fusion in remote sensing.

Provided a flexible dataset pipeline for future research.

Abstract

The extraction of visual features is an essential step in Visual Question Answering (VQA). Building a good visual representation of the analyzed scene is indeed one of the essential keys for the system to be able to correctly understand the latter in order to answer complex questions. In many fields such as remote sensing, the visual feature extraction step could benefit significantly from leveraging different image modalities carrying complementary spectral, spatial and contextual information. In this work, we propose to add multiple image modalities to VQA in the particular context of remote sensing, leading to a novel task for the computer vision community. To this end, we introduce a new VQA dataset, named TAMMI (Text and Multi-Modal Imagery) with diverse questions on scenes described by three different modalities (very high resolution RGB, multi-spectral imaging data and synthetic aperture radar). Thanks to an automated pipeline, this dataset can be easily extended according to experimental needs. We also propose the MM-RSVQA (Multi-modal Multi-resolution Remote Sensing Visual Question Answering) model, based on VisualBERT, a vision-language transformer, to effectively combine the multiple image modalities and text through a trainable fusion process. A preliminary experimental study shows promising results of our methodology on this challenging dataset, with an accuracy of 65.56% on the targeted VQA task. This pioneering work paves the way for the community to a new multi-modal multi-resolution VQA task that can be applied in other imaging domains (such as medical imaging) where multi-modality can enrich the visual representation of a scene. The dataset and code are available at https://tammi.sylvainlobry.com/.