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 and the distances between the center and the points on the silhouette sampling with non-uniform angle intervals, thereby achieving abalanced sampling of lines in mask generation. In addition, we propose a non-uniform polar template IoU based on the contour template in polar coordinates. Experiments on both the Airbus Ship Detection Challenge dataset and the ISAIDships dataset show that SSS-Net has strong competitiveness in precision and speed for ship instance segmentation.
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…
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 …
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…
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…
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…
Adversarial Patch Attack for Ship Detection via Localized Augmentation
State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou 450046, China（空间基准状态关键实验室，遥感与地理信息工程学院，地理科学与工程学院，河南大学，郑州450046，中国）；National Key Laboratory of Integrated Technology on Aircraft Control, Beihang University, Beijing 100191, China（航空控制集成技术国家实验室，北京航空航天大学，北京100191，中国）；School of Computer and Information Engineering, Henan University, Kaifeng 475004, China（计算机与信息工程学院，河南大学，开封475004，中国）；State Key Laboratory of Spatial Datum, Henan University, Zhengzhou 450046, China（空间基准状态关键实验室，河南大学，郑州450046，中国）
Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic o…
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。
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…
Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task
Gina Cody School of Engineering and Computer Science, Concordia University（康考迪亚大学吉娜·科迪工程与计算机科学学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
In recent years, increasing attentions are paid on object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomaly. It is a challenge to effectively utilize the cross-modality information from multi-source remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Towards this end, a fast multi-source fusion detection framework is proposed in current paper. A novel distance-decay intersection over union is employed to e…
Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection
Zhejiang University（浙江大学）；Harbin Institute of Technology（哈尔滨工业大学）；Harbin Institute of Technology Suzhou Research Institute（哈尔滨工业大学苏州研究院）；North China University of Water Resources and Electric Power（华北水利水电大学）；University of Auckland（奥克兰大学）
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）
GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation
School of Internet, Anhui University（安徽大学互联网学院）；National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University（安徽大学农业生态大数据分析与应用国家工程研究中心）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences（中国科学院遥感与数字地球研究所）
The Three Gorges Dam, a massive cross-century project spans the Yangtze River by the town of Sandouping, located in Yichang, Hubei province, China, was built to provide great power, improve the River shipping, control floods in the upper reaches of the Yangtze River, and increase the dry season flow in the middle and lower reaches of the Yangtze River. Benefits are enormous and comprehensive. However, the social and environmental impacts are also immense and far-reaching to its surrounding areas. Mapping land use /land cover changed (LUCC) is critical for tracking the impacts. Remote sensing h…
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 is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multitask rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align…
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）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Federal University of Bahia (UFBA)（巴伊亚联邦大学（UFBA））
Under the sea, visible spectrum cameras have limited sensing capacity, being able to detect objects only in clear water, but in a constrained range. Considering any sea water condition, sonars are more suitable to support autonomous underwater vehicles' navigation, even in turbid condition. Despite that sonar suitability, this type of sensor does not provide high-density information, such as optical sensors, making the process of object recognition to be more complex. To deal with that problem, we propose a novel trainable method to detect and recognize (identify) specific target objects under…
In this paper a new method of image smoothing for satellite imagery and its applications in environmental remote sensing are presented. This method is based on the global gradient minimization over the whole image. With respect to the image discrete identity, the continuous minimization problem is discretized. Using the finite difference numerical method of differentiation, a simple yet efficient 5*5-pixel template is derived. Convolution of the derived template with the image in different bands results in the discrimination of various image elements. This method is extremely fast, besides bei…
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 …
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）
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…
Global-Local Detail Guided Transformer for Sea Ice Recognition in Optical Remote Sensing Images
The Academy of Digital China, Fuzhou University（福州大学数字中国研究院）；The Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education（教育部空间数据挖掘与信息共享重点实验室）
A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光学电子学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）
Southwest Jiaotong University（西南交通大学）；University of Trento（特伦托大学）；Purdue University（普渡大学）
Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first…
University of Poitiers（普瓦捷大学）；DGA/DS/QIS（法国国防部装备总署/情报处/量子信息部门）
We introduce a new constrained minimization problem that performs template and pattern detection on a multispectral image in a compressive sensing context. We use an original minimization problem from Guo and Osher that uses $L_1$ minimization techniques to perform template detection in a multispectral image. We first adapt this minimization problem to work with compressive sensing data. Then we extend it to perform pattern detection using a formal transform called the spectralization along a pattern. That extension brings out the problem of measurement reconstruction. We introduce shifted mea…
Southwest Jiaotong University（西南交通大学）；University of Trento（特伦托大学）；Purdue University（普渡大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, LiDAR, SAR, and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor, such as Histogram of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust Feature (SURF), is first extracted at each pixel to form a pixel…
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）
