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Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images

Wenxuan Han et al.

Summary: This paper proposes a multi-vision small object detector for accurate and rapid detection of airplanes, cars, and ships in remote sensing images. The method improves the feature representation ability of convolutional neural networks by introducing multiscale residual blocks, multiscale receptive field enhancement modules, and a multi-vision network. Experimental results demonstrate significant detection performance of the proposed method.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

FRPNet: A Feature-Reflowing Pyramid Network for Object Detection of Remote Sensing Images

Jingyu Wang et al.

Summary: This letter proposes an end-to-end feature-reflowing pyramid network (FRPNet) for geospatial object detection. The network incorporates a nonlocal block and a feature-reflowing pyramid structure to improve detection accuracy for multiscale and multiclass objects.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

Synthetic Data Augmentation Using Multiscale Attention CycleGAN for Aircraft Detection in Remote Sensing Images

Weixing Liu et al.

Summary: This letter proposes a practical framework for automatically generating content-rich synthetic images and enhances image quality using a multiscale attention module, with experiments showing that synthetic data augmentation can improve aircraft detection performance.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

Feature Split-Merge-Enhancement Network for Remote Sensing Object Detection

Wenping Ma et al.

Summary: In this study, a scale-aware network called SME-Net is proposed for remote sensing object detection. It consists of the FSM module, OER module, and OSE strategy to address the challenges of large and small object detection balance, feature inconsistency, and noise interference in high-resolution remote sensing images. The effectiveness of the algorithm has been demonstrated on multiple datasets.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

A New Spatial-Oriented Object Detection Framework for Remote Sensing Images

Dawen Yu et al.

Summary: In this article, a novel object detection framework RSADet is proposed for remote sensing images, considering the spatial distribution, scale, and orientation/shape variations of the objects. The framework utilizes scale-attention boosted CNN heatmaps and deformable convolutions to improve detection performance, and introduces a bounding box confidence prediction branch to eliminate unreliable boxes.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

Zhanchao Huang et al.

Summary: In this article, the authors propose a lightweight oriented object detector (LO-Det) for remote sensing object detection. They design a channel separation-aggregation (CSA) structure and a dynamic receptive field (DRF) mechanism to optimize efficiency and accuracy. They also introduce a diagonal support constraint head (DSC-Head) component to accurately and stably constrain the shape of oriented bounding boxes (OBBs). Experimental results demonstrate that LO-Det achieves fast runtime and competitive accuracy on embedded devices.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Center-Boundary Dual Attention for Oriented Object Detection in Remote Sensing Images

Shuai Liu et al.

Summary: This article proposes a novel anchor-free detector, CBDA-Net, for oriented object detection on remote sensing images. CBDA-Net utilizes a dual attention mechanism to extract attention features on the center and boundary regions of objects, learning essential features for rotating objects and reducing interference from complex backgrounds. Additionally, an aspect ratio weighted angle loss is introduced to address the influence of object aspect ratio on angle errors.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

CANet: Centerness-Aware Network for Object Detection in Remote Sensing Images

Lukui Shi et al.

Summary: In this study, we propose a feature pyramid-based remote sensing object detector called Centerness-Aware Network (CANet), which captures the symmetrical shape of objects in remote sensing images. CANet integrates Multiscale Centerness Descriptor (MSCD), Centerness Detection Head (CDH), and Feature Selective Module (FSM) into the feature pyramid to extract and utilize features around the center region. Experiments show that CANet is competitive with some state-of-the-art detection networks.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Laplacian Feature Pyramid Network for Object Detection in VHR Optical Remote Sensing Images

Wenhua Zhang et al.

Summary: The paper introduces a novel Laplacian Feature Pyramid Network (LFPN) that combines low-frequency and high-frequency features to enhance object detection performance in VHR-ORS images. High-frequency features, crucial for distinguishing ground objects, have not been adequately addressed in previous studies.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Few-Shot Object Detection on Remote Sensing Images

Xiang Li et al.

Summary: In this article, a metalearning-based method for few-shot object detection on remote sensing images is introduced. Experimental results on benchmark datasets demonstrate that the proposed method achieves satisfying detection performance with only a few annotated samples and outperforms existing baseline models.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

FSoD-Net: Full-Scale Object Detection From Optical Remote Sensing Imagery

Guanqun Wang et al.

Summary: The paper introduces a novel one-stage object detection network, which has achieved good performance in processing optical remote sensing images. By utilizing the improved multiscale enhancement network and scale-invariant regression layers, the network effectively enhances the accuracy of object detection.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Prototype-CNN for Few-Shot Object Detection in Remote Sensing Images

Gong Cheng et al.

Summary: This article focuses on the main challenges of few-shot object detection in remote sensing images and proposes a simple yet effective method named P-CNN, which consists of a prototype learning network, a prototype-guided region proposal network, and a detector head to overcome the challenges.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Learning Efficient and Accurate Detectors With Dynamic Knowledge Distillation in Remote Sensing Imagery

Yidan Zhang et al.

Summary: This article introduces a general and effective knowledge distillation framework called Dynamic Knowledge Distillation (DKD) to address the difficulty of deploying deep convolutional neural networks on low computation edge devices. The framework utilizes dynamic global distillation and dynamic instance selection distillation modules for training multiscale feature imitation and self-judgment abilities. Experimental results demonstrate that the framework is suitable for different types of detectors and achieves state-of-the-art performance.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Environmental Sciences

GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

Yan Zhang et al.

Summary: This paper discusses the recent progress in small object detection in aerial images using convolutional neural networks. The paper introduces a transformer backbone attention mechanism and a generative model to improve detection accuracy. The results show significant improvements in precision, recall, and mAP, and the paper also presents an auto-pruning technique for real-time detection tasks.

REMOTE SENSING (2022)

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Transformer with Transfer CNN for Remote-Sensing-Image Object Detection

Qingyun Li et al.

Summary: This study investigates object detection in remote-sensing images using a Transformer-based approach. The proposed method combines CNN and Transformer, utilizing a modified Transformer to aggregate global features at multiple scales and model the interactions between instances. By incorporating data augmentation, the proposed method achieves competitive results on two widely-used datasets.

REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery

Yanfeng Liu et al.

Summary: In this article, we propose an adaptive balanced network (ABNet) to address the challenges of remote sensing object detection. Our approach utilizes an enhanced effective channel attention mechanism (EECA), an adaptive feature pyramid network (AFPN) and a context enhancement module (CEM) to improve feature representation and capture more discriminative features. Experimental results demonstrate the superior performance of our approach.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Incremental Detection of Remote Sensing Objects With Feature Pyramid and Knowledge Distillation

Jingzhou Chen et al.

Summary: In this article, a new architecture for incremental object detection is proposed based on feature pyramid and knowledge distillation. It detects objects with different scales in different layers using a feature pyramid network (FPN), and maintains the learning capability for old classes by applying knowledge distillation. Experimental results show promising performance compared to state-of-the-art methods for incremental object detection.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

SKNet: Detecting Rotated Ships as Keypoints in Optical Remote Sensing Images

Zhenyu Cui et al.

Summary: This article proposes a novel anchor-free rotated ship detection framework, SKNet, which effectively addresses the challenges of detecting rotated ships in optical remote sensing images. Through extensive experiments on three datasets, SKNet achieves state-of-the-art detection performance while being time-efficient, demonstrating the best speed-accuracy tradeoff.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Geochemistry & Geophysics

Global Context-Augmented Objection Detection in VHR Optical Remote Sensing Images

Guang Shi et al.

Summary: The article introduces geometric transform module and global contextual feature fusion module to improve the accuracy and effectiveness of object detection in remote sensing images, achieving end-to-end detection within the YOLOv3 framework.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Environmental Sciences

A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

Zhifeng Xiao et al.

Summary: The study introduces a detection method that adapts anchoring based on sample balance, selecting candidate anchors through horizontal IoU and using an adaptive threshold module to maintain a balance between positive and negative anchors, ultimately achieving better performance on a public aerial image dataset.

REMOTE SENSING (2021)

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Article Computer Science, Information Systems

YOLSO: You Only Look Small Object

Jinpu Zhang et al.

Summary: This paper introduces a small object detector named YOLSO, which addresses the issues in feature representation and loss function in small object detection by introducing methods such as the Half-Space Shortcut module and the Feature Pyramid Enhancement module, achieving excellent results on small object datasets.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2021)

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Article Geochemistry & Geophysics

Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images

Gong Cheng et al.

Summary: There are many groundbreaking object detection frameworks used in natural scene images, but applying them directly to remote sensing images is not very effective. This paper proposes an end-to-end cross-scale feature fusion (CSFF) framework to address the challenges of object detection in optical remote sensing images, achieving a 3.0% improvement in mAP on the DIOR dataset compared to Faster R-CNN with FPN.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Engineering, Electrical & Electronic

RefineDet plus plus : Single-Shot Refinement Neural Network for Object Detection

Shifeng Zhang et al.

Summary: In this research, a novel single-shot based object detector, RefineDet++, is proposed to combine the advantages of both two-stage and one-stage approaches. The method consists of two inter-connected modules: anchor refinement module and alignment detection module, which work together to improve the accuracy and efficiency of object detection. Extensive experiments on PASCAL VOC and MS COCO dataset show that RefineDet++ achieves state-of-the-art detection accuracy with high efficiency.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

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Arbitrary Direction Ship Detection in Remote-Sensing Images Based on Multitask Learning and Multiregion Feature Fusion

Qiangwei Liu et al.

Summary: This article proposes a new framework based on an improved Faster R-CNN for detecting ships in arbitrary directions. The method combines global and local features of proposal regions through a multi-region feature-fusion module, and achieves ship bounding-box recognition through multitask learning. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art ship-detection methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block

Zhichao Yuan et al.

Summary: The proposed Multi-Feature Pyramid Network (MFPNet) with Receptive Field Block (RFB) effectively addresses the challenges of object detection in optical remote sensing images. Experimental results on the Levir and DIOR datasets demonstrate that the method outperforms state-of-the-art networks, showcasing better performance in target detection.

REMOTE SENSING (2021)

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An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation

Xiangkai Xu et al.

Summary: This study addresses the shortcomings of object detection and instance segmentation in remote sensing images, proposing an improved Swin transformer and SAIEC network framework to enhance accuracy. Experimental results demonstrate significant improvements in detection and segmentation capabilities.

REMOTE SENSING (2021)

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Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images

Qifan Wu et al.

Summary: The study focuses on utilizing the improved Mask R-CNN model SCMask R-CNN to recognize remote sensing images, using enhanced feature extraction structure and instance segmentation techniques, based on the DOTA dataset WFA-1400, introducing dilated convolutions to improve segmentation effect, and enhancing the accuracy of object detection.

SENSORS (2021)

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Self-Adaptive Aspect Ratio Anchor for Oriented Object Detection in Remote Sensing Images

Jie-Bo Hou et al.

Summary: In this study, a novel Self-Adaptive Aspect Ratio Anchor (SARA) is proposed to explore aspect ratio variations of objects in remote sensing images, along with an Oriented Box Decoder (OBD) to encode orientation information of oriented objects. The method achieves a promising mAP value of 79.91% on the Dota dataset.

REMOTE SENSING (2021)

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Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images

Hui Wang et al.

Summary: Fully supervised object detection methods in remote sensing images require a large number of annotated samples. Weakly supervised learning using only image-level annotations has attracted attention, with most methods being based on multi-instance learning approaches. Performance relies on the scoring of candidate region proposals during training.

REMOTE SENSING (2021)

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ZoomInNet: A Novel Small Object Detector in Drone Images with Cross-Scale Knowledge Distillation

Bi-Yuan Liu et al.

Summary: The paper introduces a novel cross-scale knowledge distillation method named ZoomInNet to enhance the detection of small objects. By utilizing feature pyramid network structure and layer adaption mechanisms, it achieves cross-scale information compression, significantly improving the accuracy in detecting small objects.

REMOTE SENSING (2021)

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DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images

Peng Wang et al.

Summary: This study focuses on object detection and classification of small and dense objects in remote sensing images using deep neural networks. By utilizing updated deformable convolution layers and center-point network, along with dynamic gradient adjustment and CIoU loss function, the model effectively addresses the quantity imbalance between easy and hard examples, as well as positive and negative examples, resulting in more accurate detection results.

REMOTE SENSING (2021)

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Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

Syed Muhammad Arsalan Bashir et al.

Summary: This paper introduces a novel small object detection method using image super-resolution and deep learning. By incorporating a cyclic GAN and residual feature aggregation network, the detection performance is significantly improved, as demonstrated in experiments on satellite and aerial images.

REMOTE SENSING (2021)

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Object Detection Based on Efficient Multiscale Auto-Inference in Remote Sensing Images

Shaojing Zhang et al.

Summary: The article introduces an automatic multiscale inference framework to balance the speed and accuracy of object detection in remote sensing images. By using a key-point network and attention mechanism, processing multiscale objects without being affected by the image input resolution.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Geochemistry & Geophysics

TCANet: Triple Context-Aware Network for Weakly Supervised Object Detection in Remote Sensing Images

Xiaoxu Feng et al.

Summary: A novel triple context-aware network (TCANet) is proposed for weakly supervised object detection in remote sensing images, achieving superior detection performance by collaborating global context-aware enhancement and dual local context residual modules. Comprehensive experiments demonstrate significant improvements over state-of-the-art methods on challenging data sets.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Orientation-Aware Vehicle Detection in Aerial Images via an Anchor-Free Object Detection Approach

Furong Shi et al.

Summary: In this article, a one-stage, anchor-free detection approach is proposed for detecting arbitrarily oriented vehicles in high-resolution aerial images. The method transforms the vehicle detection task into a multitask learning problem by directly predicting high-level vehicle features, such as vehicle orientations, scales, and offsets. The approach effectively addresses scenes with dense vehicles or clutter backgrounds through multiple subtasks for predicting vehicle characteristics.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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OPD-Net: Prow Detection Based on Feature Enhancement and Improved Regression Model in Optical Remote Sensing Imagery

Yanan You et al.

Summary: OPD-Net is an omnidirectional prow detection network that addresses issues in ship heading prediction through feature enhancement and improved regression model, with experiments demonstrating its robustness and superiority.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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DCL-Net: Augmenting the Capability of Classification and Localization for Remote Sensing Object Detection

Enhai Liu et al.

Summary: A new decoupled classification localization network (DCL-Net) is proposed to enhance the detection accuracy of objects in remote sensing images by considering the different characteristics between the two branches and developing two modules to suppress the strong coupling relationship.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Adaptive multi-level feature fusion and attention-based network for arbitrary-oriented object detection in remote sensing imagery

Luchang Chen et al.

Summary: A novel arbitrary-oriented object detection framework is proposed, consisting of three main parts: Cascading Attention Network, Adaptive Feature Concatenate Network, and OBB Multi-Definition and Selection Strategy. Experimental results demonstrate superior performances in multi-orientated objects detection compared with representative methods.

NEUROCOMPUTING (2021)

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Document image classification: Progress over two decades

Li Liu et al.

Summary: Document image classification is crucial in document image processing systems. This paper provides a comprehensive survey of the progress in this field over the past two decades, categorizing document images into non-mobile and mobile images and reviewing existing classification methods. The performance of different methods is compared on public benchmark datasets, with recommendations for future research directions.

NEUROCOMPUTING (2021)

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A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images

Yangyang Li et al.

Summary: A lightweight keypoint-based oriented object detector for remote sensing images is proposed in this paper, which improves detection performance by introducing a semantic transfer block and an adaptive Gaussian kernel, and obtains a lightweight student network using distillation loss associated with object detection. Experimental results show that the method adapts to objects of different scales, obtains accurate bounding boxes, and reduces the influence of complex backgrounds. The comparison with mainstream methods demonstrates comparable performance under lightweight conditions.

REMOTE SENSING (2021)

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ADT-Det: Adaptive Dynamic Refined Single-Stage Transformer Detector for Arbitrary-Oriented Object Detection in Satellite Optical Imagery

Yongbin Zheng et al.

Summary: This research proposed an adaptive dynamic refined single-stage transformer detector to address the challenges of detecting arbitrary-oriented and multi-scale objects in satellite optical imagery. By introducing a feature pyramid transformer and special post-processing steps, the detector achieved state-of-the-art detection accuracy and speed.

REMOTE SENSING (2021)

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Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images

Youyou Li et al.

Summary: The study proposed a point-based weakly supervised learning method to address the object detection challenge in high spatial resolution remote sensing images. By introducing point labels, generating pseudobounding boxes, and using a progressive candidate bounding box mining strategy, optimized object detection performance was achieved.

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AMR-Net: Arbitrary-Oriented Ship Detection Using Attention Module, Multi-Scale Feature Fusion and Rotation Pseudo-Label

Yifan Wu et al.

Summary: This paper presents a multi-task rotation detector AMR-Net, which addresses the challenges in ship detection by utilizing techniques such as attention module, multi-scale feature fusion, and rotation pseudo-label. The detector achieves excellent performance in ship detection tasks.

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DE-CycleGAN: An Object Enhancement Network for Weak Vehicle Detection in Satellite Images

Peng Gao et al.

Summary: In this article, a novel model called DE-CycleGAN is proposed to enhance weak targets for accurate vehicle detection in satellite imagery. The model implements enhancements at both image level and object level, resulting in significantly improved detection performance for weak targets.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Cross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery

Yangyang Li et al.

Summary: This article proposes a cross-layer attention network for better small object detection, achieving improved performance through feature pyramid design and attention module, with excellent experimental results compared to other detectors.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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YOLOrs: Object Detection in Multimodal Remote Sensing Imagery

Manish Sharma et al.

Summary: Deep-learning object detection methods designed for computer vision applications perform poorly on remote sensing data due to difficulties in collecting training data, small target sizes, and arbitrary perspective transformations. Fusion of data from multiple remote sensing modalities can improve detection performance. YOLOrs is a new convolutional neural network specifically designed for real-time object detection in multimodal remote sensing imagery, capable of detecting objects at multiple scales and predicting target orientations.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Automatic Weakly Supervised Object Detection From High Spatial Resolution Remote Sensing Images via Dynamic Curriculum Learning

Xiwen Yao et al.

Summary: This article proposes a dynamic curriculum learning strategy to progressively learn object detectors by increasing the difficulty of training images to match the current detection ability. By ranking training images in ascending order of difficulty and selecting easy images to provide reliable instances, the detection ability can be enhanced gradually.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Mask R-CNN

Kaiming He et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

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Focal Loss for Dense Object Detection

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Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery

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Object Detection in High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks With Suitable Object Scale Features

Zhipeng Dong et al.

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PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

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Orientation guided anchoring for geospatial object detection from remote sensing imagery

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Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

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RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images

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Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

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Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images

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