The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.
Huazhong University of Science and Technology（华中科技大学）；Qingdao University（青岛大学）
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g. the rotation angle) or a few (e.g. several coordinates) groundtruth values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To t…
Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art performance. However, established object detectors require complete, precise, and correct bounding box annotations for training. In order to create the necessary training annotations for object detectors, imagery can be georeferenced and combined with data from other sources, such as points of interest localized by GPS sensors. Unfortunately, this combination often leads to poor …
Beijing Institute of Technology（北京理工大学）；University of Delaware（特拉华大学）；The Aviation Industry Corporation of China, Ltd.（中国航空工业集团有限公司）；Luoyang Institute of Electro-Optical Equipment（洛阳电光设备研究所）
A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation …
OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing Images
School of Computer Science and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气与计算机科学学院）
MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image
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（云南软件工程重点实验室）
针对多光谱定向目标检测中模态差异与计算开销问题，提出轻量级框架 MO R-CNN，通过异构特征提取、单模态监督和条件标签融合提升检测性能。
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰瑞尔公司）
Object detection in optical remote sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks have made good progress. However, due to the large variation in object scale, aspect ratio, and arbitrary orientation, the detection performance is difficult to be further improved. In this paper, we discuss the role of discriminative features in object detection, and then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy from three aspects: building powerful feature representation, refining preset anchors…
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）
Oriented object detection in remote sensing images has made great progress in recent years. However, most of the current methods only focus on detecting targets, and cannot distinguish fine-grained objects well in complex scenes. In this technical report, we analyzed the key issues of fine-grained object recognition, and use an oriented feature alignment network (OFA-Net) to achieve high-performance fine-grained oriented object recognition in optical remote sensing images. OFA-Net achieves accurate object localization through a rotated bounding boxes refinement module. On this basis, the bound…
Fourier Angle Alignment for Oriented Object Detection in Remote Sensing
Beijing Institute of Technology（北京理工大学）；University of Hong Kong（香港大学）；Tohoku University（东北大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Hong Kong Generative AI Research and Development Center（香港生成式人工智能研究与发展中心）
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Space Mechanics and Electricity（北京空间机电研究所）；Donghai Laboratory（东海实验室）
Real-Time Oriented Object Detection Transformer in Remote Sensing Images
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116, China（计算机科学与技术学院/人工智能学院，中国矿业大学，徐州221116，中国）；Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China（教育部矿山数字化工程研究院，徐州221116，中国）；Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space, Xuzhou 221116, China（江苏省智能感知与应急物联网地下空间工程技术研究中心，徐州221116，中国）；School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada（电气工程与计算机科学学院，渥太华大学，渥太华，ON K1N 6N5，加拿大）
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Technical University of Munich (TUM)（慕尼黑工业大学）；Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）
Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-b…
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Science（中国科学院大学）
Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Obje…
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）
Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxes, then Intersection-over-Union (IoU) is applied to sample the positive and negative candidates for training. However, we observe that the selected positive anchors cannot always ensure accurate detections after regression, while some negative samples can achieve accurate localiz…
HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing
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 Iceland（冰岛大学）；Griffith University（格里菲斯大学）
Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, and the long-range context required by different types of objects can vary. In this paper, we take these priors into account and propose the Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial re…
Northwestern Polytechnical University（西北工业大学）；Zhengzhou Institute of Surveying and Mapping（郑州测绘学院）
Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, detracting from the robustness of detectors, etc. In this paper, we propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons horizontal box-related oper…
Beijing Institute of Technology（北京理工大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Hubei University of Economics（湖北经济学院）；Luoyang Institute of Electro-Optical Equipment, Aviation Industry Corporation of China, Ltd.（中国航空工业集团有限公司洛阳电光设备研究所）
Arbitrary-oriented object detection (AOOD) plays a significant role for image understanding in remote sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multi-grained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to ref…
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences（中国科学院光电信息处理重点实验室）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）
Ensemble methods are a reliable way to combine several models to achieve superior performance. However, research on the application of ensemble methods in the remote sensing object detection scenario is mostly overlooked. Two problems arise. First, one unique characteristic of remote sensing object detection is the Oriented Bounding Boxes (OBB) of the objects and the fusion of multiple OBBs requires further research attention. Second, the widely used deep learning object detectors provide a score for each detected object as an indicator of confidence, but how to use these indicators effectivel…
Benefiting from the great success of deep learning in computer vision, CNN-based object detection methods have drawn significant attentions. Various frameworks have been proposed which show awesome and robust performance for a large range of datasets. However, for building detection in remote sensing images, buildings always pose a diversity of orientations which makes it a challenge for the application of off-the-shelf methods to building detection. In this work, we aim to integrate orientation regression into the popular axis-aligned bounding-box detection method to tackle this problem. To a…
Prototype-Based Pseudo-Label Denoising for Source-Free Domain Adaptation in Remote Sensing Semantic Segmentation
College of Computer Science, Sichuan University（四川大学计算机科学学院）；College of Computer Science and Cyber Security, Chengdu University of Technology（成都理工大学计算机科学与网络安全学院）
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
Arbitrary-oriented objects exist widely in natural scenes, and thus the oriented object detection has received extensive attention in recent years. The mainstream rotation detectors use oriented bounding boxes (OBB) or quadrilateral bounding boxes (QBB) to represent the rotating objects. However, these methods suffer from the representation ambiguity for oriented object definition, which leads to suboptimal regression optimization and the inconsistency between the loss metric and the localization accuracy of the predictions. In this paper, we propose a Representation Invariance Loss (RIL) to o…
University of Alberta（阿尔伯塔大学）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学（慕尼黑大学））；Alberta Geological Survey（阿尔伯塔地质调查局）；Alberta Energy Regulator（阿尔伯塔能源监管局）
The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-re…
Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection
Zhejiang Lab（之江实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Changsha University of Science and Technology（长沙理工大学）；The Education University of Hong Kong（香港教育大学）；South China Agricultural University（华南农业大学）
Destriping is a classical problem in remote sensing image processing. Although considerable effort has been made to remove stripes, few of the existing methods can eliminate stripe noise with arbitrary orientations. This situation makes the removal of oblique stripes in the higher-level remote sensing products become an unfinished and urgent issue. To overcome the challenging problem, we propose a novel destriping model which is self-adjusted to different orientations of stripe noise. First of all, the oriented variation model is designed to accomplish the stripe orientation approximation. In …
Univ. Savoie Mont Blanc, LISTIC（萨瓦勃朗峰大学 LISTIC 实验室）；ONERA, The French Aerospace Lab（法国航空航天实验室 ONERA）；Univ. Bretagne-Sud, UMR 6074, IRISA（南布列塔尼大学 IRISA 研究所）
With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness. Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data. Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation. Various 2D architectures are tested and a new 3D model is introduced in order to jointly process the spatial and spectral dimensions of the data. Su…
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., remote sensing image) has attracted great attention in remote sensing (RS). Most of the existing methods assume that a reliable multi-modal training set with accurately matched text-image pairs is existing. However, this assumption may not always hold since the multi-modal training sets may include noisy pairs (i.e., textual descriptions/captions associated to training images can be noisy), distorting the learning …
University of Maryland（马里兰大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；US Census Bureau（美国人口普查局）
We develop an algorithm for single-image superresolution of remotely sensed data, based on the discrete shearlet transform. The shearlet transform extracts directional features of signals, and is known to provide near-optimally sparse representations for a broad class of images. This often leads to superior performance in edge detection and image representation when compared to isotropic frames. We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SME). Our algori…
Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parament or eight-parament representations, which suffer from representation ambiguity for orientated object definition. In this paper, we propose a novel representation method based on area ratio of parallelogram, called ARP. Specifically, ARP regresses the minimum bounding rectangle of the oriented object and three area ratios. Three area ratios include the a…
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Wollongong（沃林戈大学）；SAIS, Shanghai Jiao Tong University（上海交通大学国际关系学院）
Technical University of Munich（慕尼黑工业大学）；Madras Institute of Technology, Anna University（安娜大学马德拉斯理工学院）
A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We propose a novel two-step framework towards this aim. The first step involves grasp area estimation by segmentation. We receive grasp area demonstrations for a new task via interactive segmentation, and learn from these few demonstrations to estimate the required grasp area on an…
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects…
Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval
College of Science, Zhejiang University of Technology（浙江工业大学理学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
