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

arXiv: https://arxiv.org/abs/2102.07004 · HTML: https://mlsanity.com/paper/2102.07004/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification](https://mlsanity.com/paper/1807.11245/) (2018)
- [Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning](https://mlsanity.com/paper/1806.00894/) (2018)
- [Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network](https://mlsanity.com/paper/1909.13299/) (2019)
- [EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies](https://mlsanity.com/paper/1711.03954/) (2017)

## Built on by (most cited first, 8 of 8)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee](https://mlsanity.com/paper/2303.06815/) (2023)
- [An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image Classification](https://mlsanity.com/paper/2406.00348/) (2024)
- [Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour](https://mlsanity.com/paper/2211.03051/) (2022)
- [Improving generalization in reinforcement learning through forked agents](https://mlsanity.com/paper/2212.06451/) (2022)
- [FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning](https://mlsanity.com/paper/2403.10807/) (2024)
- [On filter design in deep convolutional neural network](https://mlsanity.com/paper/2410.21644/) (2024)
- [Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms](https://mlsanity.com/paper/2503.08960/) (2025)

## Abstract (arXiv)

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.