How Can Multimodal Remote Sensing Datasets Transform Classification via SpatialNet-ViT?

TL;DR

This paper introduces SpatialNet-ViT, a novel model combining Vision Transformers and Multi-Task Learning to improve remote sensing classification accuracy and generalization across diverse datasets.

Contribution

The paper presents SpatialNet-ViT, a new model that integrates spatial and contextual features for remote sensing classification, enhancing robustness and scalability.

Findings

Improved classification accuracy across multiple remote sensing tasks.

Enhanced model robustness through data augmentation and transfer learning.

Better generalization across diverse datasets.

Abstract

Remote sensing datasets offer significant promise for tackling key classification tasks such as land-use categorization, object presence detection, and rural/urban classification. However, many existing studies tend to focus on narrow tasks or datasets, which limits their ability to generalize across various remote sensing classification challenges. To overcome this, we propose a novel model, SpatialNet-ViT, leveraging the power of Vision Transformers (ViTs) and Multi-Task Learning (MTL). This integrated approach combines spatial awareness with contextual understanding, improving both classification accuracy and scalability. Additionally, techniques like data augmentation, transfer learning, and multi-task learning are employed to enhance model robustness and its ability to generalize across diverse datasets