Ship orientation angle prediction (SOAP) with optical remote sensing images is an important image processing task, which often relies on deep convolutional neural networks (CNNs) to make accurate predictions. This paper proposes a novel framework to reduce the model sizes and computational costs of SOAP models without harming prediction accuracy. First, a new SOAP model called Mobile-SOAP is designed based on MobileNetV2, achieving state-of-the-art prediction accuracy. Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively. Then, to transfer knowledge from Mobile-SOAP to four lightweight models, we propose a novel knowledge distillation (KD) framework termed SOAP-KD consisting of a novel feature-based guidance loss and an optimized synthetic samples-based knowledge transfer mechanism. Lastly, extensive experiments on the FGSC-23 dataset confirm the superiority of Mobile-SOAP over existing models and also demonstrate the effectiveness of SOAP-KD in improving the prediction performance of four specially designed tiny models. Notably, by using SOAP-KD, the test mean absolute error of the ShuffleNetV2x1.0-based model is only 8% higher than that of Mobile-SOAP, but its number of parameters and multiply-accumulate operations (MACs) are respectively 61.6% and 60.8% less.
Ship detection has been an active and vital topic in the field of remote sensing for a decade, but it is still a challenging problem due to the large scale variations, the high aspect ratios, the intensive arrangement, and the background clutter disturbance. In this letter, we propose a locality-aware rotated ship detection (LARSD) framework based on a multi-scale convolutional neural network (CNN) to tackle these issues. The proposed framework applies a UNet-like multi-scale CNN to generate multi-scale feature maps with high-level semantic information in high resolution. Then, a rotated ancho…
Intelligent System Lab, Beijing University of Posts
Ship detection using high-resolution remote sensing images is an important task, which contribute to sea surface regulation. The complex background and special visual angle make ship detection relies in high quality datasets to a certain extent. However, there is few works on giving both precise classification and accurate location of ships in existing ship detection datasets. To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD. The dataset collects high-resolution remote sensing images that containing ship samples…
Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyper-parameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in thi…
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（电子工程系，北京信息科学与技术国家研究中心，清华大学）；Chang Guang Satellite Technology Co., Ltd. (CGSTL)（长光卫星技术有限公司）
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Computer Vision and Learning Systems, Department of Electrical Engineering, Linköping University（计算机视觉与学习系统，电气工程系，利厄普大学）；Helmholtz-Zentrum Dresden-Rossendorf, Responsible AI Group（海姆霍尔茨·德累斯顿-罗斯托克研究中心，负责任人工智能小组）；University of Iceland, Faculty of Electrical and Computer Engineering（冰岛大学，电气与计算机工程学院）
Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Artificial Intelligence Thrust, Hong Kong University of Science and Technology（香港科技大学（广州）人工智能学科）
RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement
McGill University（麦吉尔大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）
Remote sensing (RS) image scene classification task faces many challenges due to the interference from different characteristics of different geographical elements. To solve this problem, we propose a multi-branch ensemble network to enhance the feature representation ability by fusing features in final output logits and intermediate feature maps. However, simply adding branches will increase the complexity of models and decline the inference efficiency. On this issue, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main-branch in it. Through optimizing wi…
College of Computing and Informatics, Sungkyunkwan University（顺天大学计算与信息学院）；Department of Computer Science and Engineering, University of Michigan（密歇根大学计算机科学与工程系）；Konkuk Aerospace Design-Airworthiness Institute, Konkuk University（konkuk大学航空航天设计-适航研究所）；Department of Electronic Systems, Aalborg University（奥尔堡大学电子系统系）；Department of Information and Communication Engineering, Noakhali Science and Technology University（诺阿克利科学与技术大学信息与通信工程系）
National University of Defense Technology（国防科技大学）；College of Electronic Science and Technology（电子科学与技术学院）
High-resolution remote sensing images can provide abundant appearance information for ship detection. Although several existing methods use image super-resolution (SR) approaches to improve the detection performance, they consider image SR and ship detection as two separate processes and overlook the internal coherence between these two correlated tasks. In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet. In our method, we not only feed the super-resolved images to the detector but also integrate the inte…
Applications of Knowledge Distillation in Remote Sensing: A Survey
College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；University of Dubai（迪拜大学）；United Arab Emirates University（阿联酋大学）；Harokopio University of Athens（雅典哈科皮奥大学）
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the …
Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN) based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detection via the rotated bounding box. However, it further increases the difficulty of detection, because an additional variable of ship orientation must be accurately predicted in the algorithm. In this paper, a novel CNN-based ship detection method is proposed, by overcoming some comm…
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task. Significant efforts have been devoted to addressing this problem and achieved great progress, yet counting the number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in a natural scene, this task is challenging in the following factors: large scale variation, complex cluttered background, and orientation arbitrariness. More importa…
Austrian Institute of Technology（奥地利技术研究所）；HCM University of Technology（胡志明市理工大学）；University of Essex（埃塞克斯大学）；FPT Soft Company（FPT软件公司）；Singapore Institute of Technology（新加坡理工大学）
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance…
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学学院，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Remote Sensing Object Counting with Online Knowledge Learning
Nanjing University of Information Science and Technology（南京信息工程大学）；Nanjing Forestry University（南京林业大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN…
College of Information Science & Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）
Object detection and instance segmentation in remote sensing images is a fundamental and challenging task, due to the complexity of scenes and targets. The latest methods tried to take into account both the efficiency and the accuracy of instance segmentation. In order to improve both of them, in this paper, we propose a single-shot convolutional neural network structure, which is conceptually simple and straightforward, and meanwhile makes up for the problem of low accuracy of single-shot networks. Our method, termed with SSS-Net, detects targets based on the location of the object's center a…
Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科交叉前沿研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology（北京理工大学天基智能信息处理全国重点实验室）
本文提出统一视觉-语言模型 Popeye，通过统一标注、混合专家编码、视觉-语言对齐与指令适配实现遥感多源船舶检测，并集成 SAM 支持像素级分割。
Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between …
Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction
Universitat de València（瓦伦西亚大学）；European Space Agency（欧洲空间局）；Universidad Nacional de Colombia（哥伦比亚国立大学）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
High-resolution optical and acoustic remote sensing datasets of the Puck Lagoon, Southern Baltic
Gdynia Maritime University（格丁尼亚海事大学）；Maritime Institute（海事研究所）；Cyprus University of Technology（塞浦路斯理工大学）；Technische Universität Berlin（柏林工业大学）；Gdańsk University of Technology（格但斯克理工大学）；Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学）；Centre for Underwater Archaeology（水下考古中心）；University of Adelaide（阿德莱德大学）；Technical University Wien（维也纳工业大学）；MEWO SA（MEWO股份公司）；National Technical University of Athens（雅典国立技术大学）
We deal with the problem of information fusion driven satellite image/scene classification and propose a generic hallucination architecture considering that all the available sensor information are present during training while some of the image modalities may be absent while testing. It is well-known that different sensors are capable of capturing complementary information for a given geographical area and a classification module incorporating information from all the sources are expected to produce an improved performance as compared to considering only a subset of the modalities. However, t…
DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation
Department of Computer Science, Colorado State University, USA（计算机科学系，科罗拉多州立大学）；Department of Soil and Crop Sciences, Colorado State University, USA（土壤与作物科学系，科罗拉多州立大学）
