Unlocking the capabilities of explainable few‑shot learning in remote sensing
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for image-based remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To ov...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/178366 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | Recent advancements have significantly improved the efficiency and effectiveness of deep
learning methods for image-based remote sensing tasks. However, the requirement for
large amounts of labeled data can limit the applicability of deep neural networks to existing
remote sensing datasets. To overcome this challenge, few-shot learning has emerged as
a valuable approach for enabling learning with limited data. While previous research has
evaluated the effectiveness of few-shot learning methods on satellite-based datasets, little
attention has been paid to exploring the applications of these methods to datasets obtained
from Unmanned Aerial Vehicles (UAVs), which are increasingly used in remote sensing
studies. In this review, we provide an up-to-date overview of both existing and newly proposed
few-shot classification techniques, along with appropriate datasets that are used for
both satellite-based and UAV-based data. We demonstrate few-shot learning can effectively
handle the diverse perspectives in remote sensing data. As an example application, we
evaluate state-of-the-art approaches on a UAV disaster scene dataset, yielding promising
results. Furthermore, we highlight the significance of incorporating explainable AI (XAI)
techniques into few-shot models. In remote sensing, where decisions based on model predictions
can have significant consequences, such as in natural disaster response or environmental
monitoring, the transparency provided by XAI is crucial. Techniques like attention
maps and prototype analysis can help clarify the decision-making processes of these complex
models, enhancing their reliability. We identify key challenges including developing
flexible few-shot methods to handle diverse remote sensing data effectively. This review
aims to equip researchers with an improved understanding of few-shot learning’s capabilities
and limitations in remote sensing, while pointing out open issues to guide progress in
efficient, reliable and interpretable data-efficient techniques. |
---|