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.
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Jožef Stefan Institute（约热夫·斯泰凡研究所）；Jožef Stefan International Postgraduate School（约热夫·斯泰凡国际研究生学院）；Trinity University（三一大学）；Singidunum University（辛吉杜努姆大学）
Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
Hamburg University of Technology（汉堡工业大学）；German Aerospace Center（德国航空航天中心）；Delft University of Technology（代尔夫特理工大学）；Imperial College London（伦敦帝国理工学院）；Technische Universität Berlin（柏林工业大学）
Machine Learning Department（机器学习系）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫比内扎伊德人工智能大学）；Remote Sensing Department（遥感系）；National Center of Space Research and Technology（国家航天研究与技术中心）；Department of Mathematical Sciences（数学科学系）；Purdue University Fort Wayne（普渡大学富尔顿分校）；Department of Mathematics and Statistics（数学与统计学系）；University of Minnesota Duluth（明尼苏达大学杜鲁斯分校）；Department of Mathematics（数学系）；Nazarbayev University（纳扎尔巴耶夫大学）
National Technical University of Athens（雅典国立技术大学）；University College London（伦敦大学学院）；NCSR Demokritos（德谟克利特国家科学研究中心）
Leon A. Gatys等人，艺术风格的神经算法，已经成为学术文献和工业应用的热门话题。神经风格转移（NST）是一个重要的工具，用于广泛的应用，如艺术风格化的2D图像，用户辅助创建工具和娱乐应用的生产工具。本研究的目的是提出一种方法，从卫星图像创建艺术地图，基于NST算法。该方法包括三个基本步骤：（i）在原始卫星图像上应用语义图像分割，将其内容划分为类别（即土地，水），（ii）对每个类别应用神经风格转移，以及（iii）创建拼贴画，即由前一步骤生成的两个风格化图像的组合组成的艺术图像。
Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（地球观测数据科学教授团，慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Image Processing Laboratory (IPL), Universitat de València (UV)（图像处理实验室（IPL），瓦伦西亚大学）；University of Kaiserslautern-Landau, Germany（德国凯撒斯劳滕-兰道大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；School of Applied Mathematics, Getulio Vargas Foundation, Brazil（巴西格洛里奥·瓦格斯基金会应用数学学院）；Institute for Explainable Machine Learning at Helmholtz Munich（海德堡慕尼黑可解释机器学习研究所）；Chair of Interpretable and Reliable Machine Learning, Technical University of Munich（可解释和可靠机器学习教授团，慕尼黑技术大学）
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
Bias Variance Labs, d.o.o.（Bias Variance Labs有限责任公司）；Faculty of Computer Science and Engineering, University Ss Cyril and Methodius（圣西里尔与美多德大学计算机科学与工程学院）；Jožef Stefan Institute（约泽夫·斯蒂芬研究所）；University of Cambridge（剑桥大学）
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Liaocheng University（聊城大学）
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Massachusetts Institute of Technology（麻省理工学院）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Xinterra；National University of Singapore（新加坡国立大学）
No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling
Institute of Mathematics and Computer Science（数学与计算机科学研究所）；University of São Paulo（圣保罗大学）；São Carlos School of Engineering（圣卡洛斯工程学院）；Institute of Informatics（信息学研究所）；Federal University of Goiás（戈亚斯联邦大学）
Multimodal Fusion Transformer for Remote Sensing Image Classification
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Technical University of Munich（慕尼黑工业大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Extremadura（埃斯特雷马杜拉大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
