Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of …
Panoptic segmentation combines instance and semantic predictions, allowing the detection of "things" and "stuff" simultaneously. Effectively approaching panoptic segmentation in remotely sensed data can be auspicious in many challenging problems since it allows continuous mapping and specific target counting. Several difficulties have prevented the growth of this task in remote sensing: (a) most algorithms are designed for traditional images, (b) image labelling must encompass "things" and "stuff" classes, and (c) the annotation format is complex. Thus, aiming to solve and increase the operabi…
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Mechanical Engineering and Automation, Fuzhou University（福州大学机械工程与自动化学院）；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南大学云南省软件工程重点实验室）
Beijing Institute of Technology（北京理工大学）；German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；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（格勒诺布尔国立高等物理、数学与信息学院）；University of Iceland（冰岛大学）
With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning…
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering t…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；University of São Paulo（圣保罗大学）；Federal University of Mato Grosso do Sul（南马托格罗索联邦大学）
Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are …
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；School of Automation, Central South University（中南大学自动化学院）；Changsha University of Science and Technology（长沙理工大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Lancaster University（兰卡斯特大学）；Xidian University（西安电子科技大学）
School of Computer and Artificial Intelligence, Zhengzhou University（计算机与人工智能学院，郑州大学）；Wuhan University（武汉大学）；Harbin Institution of Technology（哈尔滨理工大学）；Dalian Maritime University（大连海事大学）；National Tsinghua University（清华大学）
Shen Yuan Honors College, Beihang University（北航沈元荣誉学院）；Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University（北京航空航天大学宇航学院航天智能科学与技术系）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology（中国空间技术研究院钱学森空间技术实验室）
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few data points remains a challenge. Existing methods for few-shot remote sensing scene classification are performed in a sample-level manner, resulting in easy overfitting of learned features to individual samples and inadequate generalization of learned category segmentation surfaces. To solve this problem, learning should be organized at the task level rather than the sample level. Learning on tasks sampled from a task…
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（格勒诺布尔理工学院）；GIPSA-lab（GIPSA实验室）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；the University of Tokyo（东京大学）；RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所革新智能综合研究中心）；Xi’an Jiaotong University（西安交通大学）；INRIA（法国国家信息与自动化研究所）；LJK（让·昆兹曼实验室）；Mississippi State University（密西西比州立大学）
Classification and identification of the materials lying over or beneath the Earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS) and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the…
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；Univ. Grenoble Alpes（格勒诺布尔大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所智能情报研究中心）；Graduate School of Frontier Sciences, the University of Tokyo（东京大学前沿科学研究生院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Wuhan University（武汉大学）；INRIA（法国国家信息与自动化研究所）；LJK（格勒诺布尔大学-法国国家科学研究中心-格勒诺布尔理工学院联合实验室）
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML)…
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University（航天智能科学与技术系，航天学院，北京航空航天大学）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（虚拟现实技术与系统国家重点实验室，北京航空航天大学）；Shenyuan Honors College, Beihang University（Shen Yuan荣誉学院，北京航空航天大学）
Remote heart rate estimation is the measurement of heart rate without any physical contact with the subject and is accomplished using remote photoplethysmography (rPPG) in this work. rPPG signals are usually collected using a video camera with a limitation of being sensitive to multiple contributing factors, e.g. variation in skin tone, lighting condition and facial structure. End-to-end supervised learning approach performs well when training data is abundant, covering a distribution that doesn't deviate too much from the distribution of testing data or during deployment. To cope with the unf…
Central South University（中南大学）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutional neural network-based (CNN-based) methods have shown enormous potential to understand remote sensing images. CNN-based methods meet with success by utilizing features learned from data rather than features designed manually. The feature-learning procedure of CNN largely depends on the architecture of CNN. However, most of the architectures of CNN used for remote sensing scene classification are still designed by h…
Zhejiang University-University of Illinois at Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳香槟分校联合学院）；Zhejiang University（浙江大学）
Multimodal sentiment analysis (MSA) is an important way of observing mental activities with the help of data captured from multiple modalities. However, due to the recording or transmission error, some modalities may include incomplete data. Most existing works that address missing modalities usually assume a particular modality is completely missing and seldom consider a mixture of missing across multiple modalities. In this paper, we propose a simple yet effective meta-sampling approach for multimodal sentiment analysis with missing modalities, namely Missing Modality-based Meta Sampling (M3…
Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review
Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校人工智能卓越中心）；Tata Consultancy Services Research（塔塔咨询服务公司研究院）
Adaptive Multi-Scale Correlation Meta-Network for Few-Shot Remote Sensing Image Classification
School of Computer Science, UPES, Dehradun, 248007, Uttarakhand, India.（UPES计算机科学学院）；School of Computer Science and Engineering, Galgotias University, India.（加尔各答大学计算机科学与工程学院）；Computer Science and Information Systems, BITS Pilani, Pilani Rajasthan, India.（比什努学院计算机科学与信息系统）
Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting
Remote Sensing Lab, National Technical University of Athens（遥感实验室，国家技术大学雅典分校）；Remote Sensing Lab, Department of Engineering and Sciences, National Technical University of Athens（遥感实验室，工程与科学系，国家技术大学雅典分校）；Universitas Mercatorum（默卡托大学）
University of Cape Town（开普敦大学）；University of Lagos（拉各斯大学）
The remote sensing community has identified data fusion as one of the key challenging topics of the 21st century. The subject of image fusion in two-dimensional (2D) space has been covered in several published reviews. However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has not been addressed till date. DEM fusion is a key application of data fusion in remote sensing. It takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate and reliable elevation dataset. Although several methods for fusing DEMs have been developed,…
