Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives

TL;DR

This paper surveys recent weight initialization techniques for deep learning in remote sensing, highlighting their importance in improving model performance and guiding future research in this specialized field.

Contribution

It is the first comprehensive survey focusing specifically on weight initialization methods for deep learning models in remote sensing applications.

Findings

Identifies key weight initialization techniques used in remote sensing

Highlights the impact of initialization on deep learning performance

Provides future research directions in the field

Abstract

During the last decade, several research works have focused on providing novel deep learning methods in many application fields. However, few of them have investigated the weight initialization process for deep learning, although its importance is revealed in improving deep learning performance. This can be justified by the technical difficulties in proposing new techniques for this promising research field. In this paper, a survey related to weight initialization techniques for deep algorithms in remote sensing is conducted. This survey will help practitioners to drive further research in this promising field. To the best of our knowledge, this paper constitutes the first survey focusing on weight initialization for deep learning models.