A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
In the last decade, the rapid development of deep learning (DL) has made it
possible to perform automatic, accurate, and robust Change Detection (CD) on
large volumes of Remote Sensing Images (RSIs). However, despite advances in CD
methods, their practical application in real-world contexts remains limited due
to the diverse input data and the applicational context. For example, the
collected RSIs can be time-series observations, and more informative results
are required to indicate the time of change or the specific change category.
Moreover, training a Deep Neural Network (DNN) requires a massive amount of
training samples, whereas in many cases these samples are difficult to collect.
To address these challenges, various specific CD methods have been developed
considering different application scenarios and training resources.
Additionally, recent advancements in image generation, self-supervision, and
visual foundation models (VFMs) have opened up new approaches to address the
'data-hungry' issue of DL-based CD. The development of these methods in broader
application scenarios requires further investigation and discussion. Therefore,
this article summarizes the literature methods for different CD tasks and the
available strategies and techniques to train and deploy DL-based CD methods in
sample-limited scenarios. We expect that this survey can provide new insights
and inspiration for researchers in this field to develop more effective CD
methods that can be applied in a wider range of contexts.
