The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Multi-class geospatial object detection and geographic image classification based on collection of part detectors
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“or to train independent detectors separately for each orientation [167, 168].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [168]
Citations
Cites
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The pascal visual object classes (VOC) challenge
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Visual categorization with bags of keypoints
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Bag-of-visual-words and spatial extensions for land-use classification
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Poselets: Body part detectors trained using 3D human pose annotations
Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961
Ensemble of exemplar-SVMs for object detection and beyond
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Items 601–700 of 1,000
Wang, Y., Li, J., Jiahui, G., Liu, R., Cao, Q., Li, D., & Wang, L. (2025) · Remote Sensing · doi:10.3390/rs17060972 · Cited by 5
A deep learning pipeline to power infrastructure detection in high-resolution satellite images
Ye, M., Ward, P. J., Plaen, J. J.-F. G. D., & Koks, E. (2025) · Big Earth Data · doi:10.1080/20964471.2025.2490408 · Cited by 5
“The NWPU VHR-10 dataset (Cheng et al., 2014) contains 3,651 instances with resolutions ranging from 0.15 m to 12 m, including small-scale critical infrastructures such as harbours and bridges.”
InfluentialLi, X., Kong, P., Chen, W., He, W., Feng, J., & Wang, J. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3585311 · Cited by 5
“During the initial phases of remote sensing scene classification, the reliance was primarily on feature extraction methods manually designed by domain experts to capture spe-cific patterns or information [25], [26], [27], [28], [29], [30].”
Pandey, G., Sharma, V. K., Chaudhary, P., Chowdary, V. M., & Udayraj (2021) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-021-01322-9 · Cited by 5
Measuring Human Decision Confidence from EEG Signals in an Object Detection Task
Li, R., Liu, L.-D., & Lu, B. (2021) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner49283.2021.9441157 · Cited by 5
“The experimental materials were obtained from two geospatial object detection datasets, NWPU VHR-10 [8] and DOTA [9], which contain two categories of objects, i.e., aircraft and ships.”
MethodJiang, Z., Yu, J., Liu, J., Chen, Z., Gui, W., & Cao, T. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3216662 · Cited by 5
“FSSD [34] combines shallow detail features with deep semantic features to reconstruct a set of pyramid features, which enhances the detection accuracy of the network.”
BackgroundAnalyzing the age-friendliness of the urban environment using computer vision methods
Moradi, F., Biloria, N. M., & Prasad, M. (2023) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231153862 · Cited by 5
“They can return numerous object classes in the output or the same image with bounding boxes of the street elements such as a person, trees, cars, etc. (Cheng et al., 2014; Khan and Al-habsi, 2020).”
BackgroundYang, P., Zhou, S., Wang, L., & Yang, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17237-1 · Cited by 5
Zhang, H., Rao, Y., Shao, J., Meng, F., & Ahmad, N. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3325410 · Cited by 5
Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint
Vo, N. D., Ngo, H.-G., Le, T. T., Nguyen, Q. H., Doan, D.-D., Nguyen, B. P., Le-Huu, D., Le-Duy, N., Tran, H., Nguyen, K.-D., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3371514 · Cited by 5
“To address these limitations, the concept of oriented bounding boxes (OBBs) has been introduced and applied in various datasets like DOTA [4], HRSC2016 [51], NWPU VHR-10 [52], DIOR-R [31], and SODA [5].”
BackgroundYang, J., & Wang, T. (2024) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.18.028503 · Cited by 5
Ran, Q., Li, M., Zhao, B., He, Z., & Wu, Y. (2024) · Remote Sensing · doi:10.3390/rs16112026 · Cited by 5
“The proposed method uses the YOLOv5s model to compare the latest target detector and pruning methods on NWPU VHR-10 [12] and RSOD [13] datasets.”
MethodA Novel Depth-Wise Separable Convolutional Model for Remote Sensing Scene Classification
Sahu, S. R., & Panda, S. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-01904-3 · Cited by 5
“…hazards detection (Martha et al., 2011; Cheng et al., 2013; Stumpf & Kerle, 2011), LULC determination (Zhu et al., 2016; Zhao et al., 2015), geospatial object detection (Cheng et al., 2014), and geographic image retrieval (Wang et al., 2016) are come to an interest in the field of remote sensing.”
Zhu, Z., Wang, P., Diao, W., Yang, J., Kong, L., Wang, H., & Sun, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3512865 · Cited by 7
Defective Pixel Corrector for Line Scan and Area Scan Image Sensors
Peng, L., Huang, Y., Wang, M., Li, W., Jing, M., & Zeng, X. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3523397 · Cited by 5
“The original images in the Set12 dataset [34] and NWPU VHR-10 dataset [35] are selected as reference images and the corresponding distorted images are constructed for evaluating different defective pixel detection and correction algorithms.”
RAFNet: Rotation-aware anchor-free framework for geospatial object detection
Deng, L., Tan, Y., & Chen, S. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104373 · Cited by 5
Yang, C., Liu, H., Wang, S., & Liao, S. (2016) · Sensors · doi:10.3390/s16030392 · Cited by 5
Accurate Object Detection Based on Faster R-CNN in Remote Sensing Imagery
Kumar, D., Zhang, X., Su, H., & Wei, S. (2019) · IEEE Asia-Pacific Conference on Synthetic Aperture Radar · doi:10.1109/apsar46974.2019.9048519 · Cited by 5
“This dataset contains 800 very-high-resolution (VHR) remote sensing images that were cropped from Google Earth and Vaihingen dataset and then manually annotated by experts [26], [27], [34].”
MethodA GF-3 SAR Image Dataset of Road Segmentation
Sun, Z., Zhao, M., & Jia, B. (2021) · Information Technology And Control · doi:10.5755/j01.itc.50.1.27987 · Cited by 7
“In the field of optical remote sensing, the public datasets mainly include NWPU-RESISC45 [3], DIOR [25], NWPU VHR-10 [2, 4, 6], DOTA [43], HRRSD [31], and RSOD [30, 44].”
MethodYang, Y., Zang, B., Li, N., Zhao, B., Li, B., & Lang, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3187724 · Cited by 5
A Robust Approach for Small-Scale Object Detection From Aerial-View
Saeed, Z., Awan, M. N. M., & Yousaf, M. H. (2022) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034601 · Cited by 5
“CornerNet [18] and ExtremeNet [12] both have been extend Object detection via aerial imaginary is an important topic where early topic [4], [5] locate objects using axis-aligned bounding boxes, but this method is ineffective for handling objects with variable rotation and dense dispersion.”
BackgroundMovable Object Detection in Remote Sensing Images via Dynamic Automatic Learning
Zhang, X., Luo, H., Zhong, S., Tang, L., Peng, J., & Jianping Fan, B. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3270496 · Cited by 5
“…DAL with eight fully supervised object detection approaches, including fast-RCNN [62], faster-RCNN [63], rotation-invariant CNN (RICNN) [64], RCNN [65], transfer CNN [66], collection of part detectors (COPD) [67], rotation-insensitive and context-augmented object detection (RICO) [17], and YOLOv5x.”
MethodVessel Detection from Optical Remote Sensing Images with Deep Learning Methods
Büyükkanber, F., Yanalak, M., & Musaoğlu, N. (2023) · International Conference on Recent Advances in Space Technologies · doi:10.1109/rast57548.2023.10197948 · Cited by 5
“This concept has been implemented on earth observation object detection applications with introducing novel open source remote sensing datasets such as DOTA [22], DIOR [23] and NHVHU [24] [25].”
MethodLand use mapping based on composite regions in aerial images
Wang, X., Su, C., Feng, C., & Zhang, X. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1500726 · Cited by 5
“Recently, novice descriptors such as sparse auto-encoder (Cheng et al. 2014) as well as fusing multiple features (Xiaoyong. et al. 2017) have been adopted in scene classification for high-resolution images (Fan., Du, and Liangpei 2015; Yanfei. et al. 2015), however,methods of this category are used…”
MethodGeospatial object detection using deep networks
Barut, O., & Alatan, A. A. (2019) · Earth Observing Systems · doi:10.1117/12.2530027 · Cited by 5
Ye, H., Cui, B., Huang, S., Dong, Y., Huang, W., Guo, A., Ren, Y., & Jin, Y. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-3947-3_19 · Cited by 5
SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch
Li, Q., Lin, Y., & He, W. (2021) · Applied Sciences · doi:10.3390/app11031096 · Cited by 5
“The experimental results on the NWPU VHR-10 dataset show that the proposed SSD7-FFAM can reach 83.7% mAP and predict an image at an average of 0.033 s.”
MethodInfluentialCATNet: Context AggregaTion Network for Instance Segmentation in Remote Sensing Images.Preprint
Liu, Y., Li, H., Hu, C., Luo, S., Shen, H., & Chen, C. W. (2021) · arXiv (Cornell University) · Cited by 5
Adaptive Hierarchical Aggregation for Federated Object Detection
Jia, R., Xie, W., Lei, J., & Li, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681158 · Cited by 4
“We extensively evaluate our method with three public datasets: VOC 2007 [6], NWPU VHR-10 [4], BCCD [38].”
MethodDataset and Benchmark for Fine-Grained Ship Recognition in Complex Optical Remote Sensing Scenarios
Wang, J., Hu, J., Zhi, X., Shi, T., & Cui, Q. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3569569 · Cited by 4
“In 2014, the NWPU VHR’10 dataset was introduced with 800 high-resolution aerial images [9].”
Text-Guided Distribution Calibration for Few-Shot Object Detection in Remote Sensing Images
Cao, Y., Chen, J., Wang, H., Lei Zhang, A., Ding, C., Wei, W., Cao, S., & Xie, M. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3582838 · Cited by 4
“NWPU VHR-10 [54] contains 800 expert hand-labeled VHR optical RSIs.”
Xu, Y., Qin, J., Zhan, T., Wu, H., Wei, Z., & Wu, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3602969 · Cited by 4
“For the NWPU VHR-10 [45] dataset, we randomly select seven categories as base classes and the remaining three categories as novel classes.”
InfluentialLi, J., Chen, Y., Niu, M., Cai, W., & qiu, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae0fb6 · Cited by 4
“To comprehensively evaluate the detection performance of the improved algorithm from different perspectives, two RS data-sets of different scales and types were selected for the experiments: iSAID [29] and NWPU VHR-10 [30].”
Wu, J., Tao, X., & Gao, P. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3649074 · Cited by 4
Zhang, S., Zhang, X., Xu, Y., & Wang, K. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3650394 · Cited by 4
“NWPU VHR − 10 is a VHR RSI dataset released by Cheng et al. [66].”
FDE-YOLO: An Improved Algorithm for Small Target Detection in UAV Images
Li, J., Guo, X., Zhao, X., & Jin, J. (2026) · Mathematics · doi:10.3390/math14040663 · Cited by 4
“Generalization experiments in this paper are verified through experiments on the UAV aerial photography dataset UAV-DT [49] and the remote sensing image dataset NWPU VHR-10 [50].”
TerraGen: A Unified Multi-Task Layout Generation Framework for Remote Sensing Data Augmentation
Tang, D., Wang, H., Xin, Y., Qiao, H., Jiang, D., Li, Y., Yu, Z., & Cao, X. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3695344 · Cited by 4
MBAN: multi-branch attention network for small object detection
Li, L., Gao, S., Wu, F., & An, X. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1965 · Cited by 4
Ma, J., Fu, D., Wang, D., & Li, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3394052 · Cited by 6
“The main dataset utilized for the experiments in this paper is the HRSC2016 dataset [41], publicly available in China.”
MethodShape Activated CAM Learning for Weakly Supervised Remote Sensing Semantic Segmentation
Chen, H., Dong, M., Yue, L., Zheng, X., Li, J., & Gong, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3578466 · Cited by 4
“To verify the effectiveness of the proposed ISANet, we first conducted experiments on the NWPU VHR-10 [38] dataset.”
FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer
Wu, W., Hu, X., Zhu Li, B., & Luo, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3581378 · Cited by 4
“…(TD) [6]) to represent global information, while others paid more attention to local descriptors (e.g., scale invariant feature transformation (SIFT) [7] and histogram of oriented gradients (HOG) [8]) which were used to capture characteristics of local regions to describe an entire image.”
WHU-RS19 ABZSL: An Attribute-Based Dataset for Remote Sensing Image Understanding
Balestra, M., Paolanti, M., & Pierdicca, R. (2025) · Remote Sensing · doi:10.3390/rs17142384 · Cited by 5
Gautam, S., Gandhi, T. K., & Panigrahi, B. K. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102993 · Cited by 4
Deep Adaptive Proposal Network in Optical Remote Sensing Images Objective Detection
Li, L., Cheng, L., Guo, X., Liu, X., Jiao, L., & Liu, F. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9324275 · Cited by 4
“In our experiments, the NWPU VHR-10[16] geospatial object detection dataset is utilized to investigate the performance of our proposed DAPNet.”
MethodYu, D., Xu, Q., Liu, X., Guo, H., Lu, J., Lin, Y., & Lv, L. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3230797 · Cited by 4
“For a long time, the horizontal bounding box (HBB) is used to represent the boundary of an object when detecting objects in remote sensing images [7], [8], [9].”
MethodMulti-scale Self-attention-based Few-shot Object Detection for Remote Sensing Images
Wang, R., Wang, Q., Yu, J., & Tong, J. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9949538 · Cited by 4
“NWPU VHR-10 is a high resolution RSI dataset released in [51].”
BackgroundChen, S., Wang, B., & Zhong, C. (2023) · EAI Endorsed Transactions on Energy Web · doi:10.4108/ew.3404 · Cited by 4
“The NWPU VHR-10 data set published by Northwestern University in 2014 [23, 24, 25] contains 10 categories of objects, that is, aircraft, ships, storage tanks, baseball diamonds, tennis courts, basketball courts, ground orbital fields, ports, bridges, and vehicles.”
MethodIceRegionShip: Optical Remote Sensing Dataset for Ship Detection in Ice-Infested Waters
Wang, P., Liu, B., Li, Y., Chen, P., & Liu, P. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3335294 · Cited by 4
“We surveyed popular optical remote sensing datasets containing ship targets, such as NWPU VHR-10 [14], DOTAv1.”
BackgroundGe, L., & Dou, L. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3404665 · Cited by 4
“…object detection in aerial images (ODAIs) [2], detection in optical remote sensing images (DIOR) [3], and Northwestern Poly-technical University very high resolution (NWPU VHR) [4], each differing in image size, data sources, number of instances, categories of instances, and ground resolution.”
BackgroundLiu, S., Wang, F., You, H., Jiao, N., Zhou, G., & Zhang, T. (2024) · Remote Sensing · doi:10.3390/rs16132472 · Cited by 4
Remote sensing object detection with feature-associated convolutional neural networks
Rao, J., Wu, T., Li, H., Zhang, J., Bao, Q., & Peng, Z. (2024) · Frontiers in Earth Science · doi:10.3389/feart.2024.1381192 · Cited by 4
Zhang, J., Zhang, W., Zhang, X., Li, Y.-H., Feng, M., Qi, P., Huang, Y.-Q., Li, R. S., Jiang, M., Zou, Y., et al. (2024) · Environmental Monitoring and Assessment · doi:10.1007/s10661-024-12947-x · Cited by 4
“The theory was designed to investigate how spatial heterogeneity affected the consistency of the spatial distribution pattern between X and Y; if X significantly influences Y, then the spatial distributions of X and Y are comparable (Cheng et al., 2014; Wang & Xu, 2017).”
Yi, Q., Zheng, M., Shi, M., Weng, J., & Luo, A. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3462089 · Cited by 4
“We evaluate our method on the DIOR [22], DOTA [23], and NWPU VHR-10 [24] datasets.”
MethodWang, F., Li, D., Zhang, J., Wang, X., Li, L., & Shi, X. (2024) · Cluster Computing · doi:10.1007/s10586-024-04823-7 · Cited by 4
RS-FeatFuseNet: An Integrated Remote Sensing Object Detection Model with Enhanced Feature Extraction
Qiu, Y., Xue, J., Zhang, G., Hao, X., Leí, T., & Jiang, P. (2024) · Remote Sensing · doi:10.3390/rs17010061 · Cited by 4
“The NWPU VHR-10 dataset [49] is a remote sensing dataset introduced by the North-western Polytechnical University (NWPU), comprising 10 categories of remotely sensed feature objects such as airplanes, ships, baseball fields, and more.”
BSFCoS: Block and Sparse Principal Component Analysis-Based Fast Co-Saliency Detection Method
Shen, N., Li, J., Zhou, P.-Y., Huo, Y., & Zhuang, Y. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800141655003x · Cited by 4
Self-citationSparse Representation and Collaborative Representation? Both Help Image Classification
Xie, W.-Y., Liu, B., Shao, S., Li, Y., & Wang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2921538 · Cited by 5
“INTRODUCTION Visual recognition has attracted intensive attention due to its high value in practical applications, such as face recognition [1], [2], medical diagnosis [3], [4], and geospatial object detection [5], [6].”
BackgroundRemote sensing scene classification with multi-spatial scale frequency covariance pooling
Chen, W., Gao, Y., Chen, A., Zhou, G., Wang, J., Yang, X., & Jiang, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12603-x · Cited by 4
Tan, Z., Jiang, Z., Chen, G., & Zhang, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3295732 · Cited by 4
“v2 [49] and HRSC2016 [50] to evaluate the effectiveness of the proposed method.”
MethodQuery-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
Chen, E., Li, M., Zhang, Q., & Chen, M. (2023) · Applied Sciences · doi:10.3390/app13179704 · Cited by 4
Yang, P., Yu, D., & Yang, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16973-8 · Cited by 5
“The NWPU VHR-10 dataset [36]”
Fusion based feature reinforcement component for remote sensing image object detection
Zhu, D., Xia, S., Zhao, J., Zhou, Y. Z., Niu, Q., Yao, R., & Chen, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08876-9 · Cited by 4
Liu, Y., Yang, J., & Cui, W. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9324301 · Cited by 4
“We use NWPU VHR-10 dataset [7], a publicly available 10-class geospatial object detection dataset cropped from Google Earth, to evaluate all methods.”
MethodFeature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images
Zhou, L., Li, Y., Rao, X., Wang, Y., Zuo, X., Qiao, B., & Yang, Y. (2022) · Electronics · doi:10.3390/electronics11040634 · Cited by 4
Blind image quality assessment via content-invariant statistical feature
Yang, Y., Cheng, G., Yu, D., & Ye, R. (2017) · Optik · doi:10.1016/j.ijleo.2017.03.029 · Cited by 4
Self-citationDeep Hash Assisted Network for Object Detection in Remote Sensing Images
Wang, M., Sun, Z., Xu, G., Ma, H., Yang, S., & Wang, W. (2020) · IEEE Access · doi:10.1109/access.2020.3024720 · Cited by 4
“NWPU VHR-10 Dataset [39]: This dataset contains totally 800 VHR remote sensing images, where the folder ’’negative image set’’ includes 150 images that do not contain any targets of the given object classes and the folder ’’positive image set’’ includes 650 images with each image containing at least one target to be d…”
BackgroundMethodFu, H., Fan, X., Yan, Z., Du, X., Jian, H., & Xu, C. (2022) · Applied Sciences · doi:10.3390/app12063114 · Cited by 4
Li, C., Ji, E., Yao, B., Li, S., & Wang, P. (2025) · IEEE Access · doi:10.1109/access.2025.3606032 · Cited by 3
“These two benchmark datasets, specifically designed for RSI object detection, were proposed by Northwestern Polytechnical University, namely DIOR [63] and NWPU VHR10.v2 [64].”
Liu, Y., Pan, J., Yang, J., Chen, T., Zhou, P., & Zhang, B. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3624148 · Cited by 3
Self-citationLi, Y., Fang, Y., Zhou, S., Long, T., Zhang, Y., Ribeiro, N. A., & Melgani, F. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3609658 · Cited by 3
“In this study, we employ two representative datasets, namely NWPU-VHR10 [28], [29], [30] and DIOR [31], to rigorously evaluate the performance of our proposed method.”
Sun, J., Liu, Z., Xie, M., Wang, Y., Yuan, R., Wu, J., Duan, S., & Wang, L. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113519 · Cited by 4
Gu, L., Fang, Q., Попов, Е. Н., Pavlov, V. A., Volvenko, S. V., Makarov, S., & Dong, G. (2026) · Astronautics · doi:10.3390/astronautics1010005 · Cited by 3
“[25] 123,287 80 118,287 5000 The DIOR [26] dataset contains 23,463 RS images and 192,472 annotated instances, along with 20 object categories: airplane (C1), airport (C2), baseball field (C3), basketball court (C4), bridge (C5), chimney (C6), dam (C7), expressway service area (C8), expressway toll…”
Enhancing UAV aerial small target detection with bidirectional dynamic multi-scale attention YOLO
Wang, Z., Lin, J., Han, Y., & Pang, Y. (2026) · The Visual Computer · doi:10.1007/s00371-026-04404-2 · Cited by 3
A Spatial-Frequency Joint Decoupling Network for Dense Small-Object Detection
Zhao, Z., Li, J., & Liu, P. (2026) · Remote Sensing · doi:10.3390/rs18081203 · Cited by 3
Sun, B., Yan, N., Zhang, F., & Shutao Li, A. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3684901 · Cited by 3
Zhang, Z., Guan, Z., Zhao, T., Shen, H., Cai, Y., Su, Z., Shang, Y., Liu, Z., Yin, J., & Xiang, L. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.04.023 · Cited by 18
“For the OVD task, we evaluate performance using the COCO-style mean Average Precision (mAP) in the test set of NWPU VHR-10 (Cheng et al., 2014).”
LiteUAV-Det: A lightweight network for robust small-object detection in complex aerial scenes
Jobaer, S., Muzahid, A. A. M., Hussain, M. A. I., Ahmed, F., Bai, X., Han, H., & Sohel, F. A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133782 · Cited by 3
DAFSDet: Dual-Attention Guided Few-Shot Object Detection in Remote Sensing Images
Gao, G., Zhang, Z., Zhang, W., Shang, Y., Dong, Y., & Xi, J. (2026) · Remote Sensing · doi:10.3390/rs18091345 · Cited by 3
Zhang, J. Y., 谢伯祥, Lin, L., Yang, L., Zhang, X., Meng, Y., Xie, X., Zhang, Y. J., & Zhang, W. (2026) · Remote Sensing · doi:10.3390/rs18111763 · Cited by 3
Renormalized Connection for Scale-Preferred Object Detection in Satellite Imagery
Zhang, F., Li, L., Jiao, L., Liu, X., Liu, F., Yang, S., & Hou, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3440881 · Cited by 3
“We conducted experiments on five different types of datasets that represent tasks with a diverse range of scale preferences, namely: 1) IPIU; 2) RSOD Aircraft [40]; 3) RSOD [40]; 4) NWPU VHR-10 [41]; and 5) MS COCO TOD-80 [12].”
MethodDEDBNet: DoG-enhanced dual-branch object detection network for remote sensing object detection
Pan, D., Zhao, J., Zhu, T., & Yuan, J. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104789 · Cited by 5
Fang, H., Zheng, L., & Xu, W. (2024) · Sensors · doi:10.3390/s24217030 · Cited by 3
Cao, Y., Tian, Y., Su, X., Xie, M., Hao, W., Wang, H., & Wang, F. (2025) · Remote Sensing · doi:10.3390/rs17020242 · Cited by 3
Wavefront-coded image restoration technology based on multi-scale deep neural networks
Zhen, Z., Yuan, Y., Zhang, B., Liang, Z., Li, C., Piao, M., Dong, K., & Jiang, H. (2025) · Optics Express · doi:10.1364/oe.560884 · Cited by 3
Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework
Yu, H., Yin, W., Bi, H., Li, C., Feng, Y., Diao, W., & Sun, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3563641 · Cited by 4
Zhu, P., & Zhou, W. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103188 · Cited by 3
İlmak, D., Bakırman, T., & Sertel, E. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111854 · Cited by 3
DMSDA-YOLO: Dynamic Multiscale Dilated Attention for Remote Sensing Object Detection
Huang, Z., Xu, Z., Li, X., Zhang, Y., Shi, Y., Li, Q., & Fang, H. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3596809 · Cited by 3
“…[18], YOLOv6 [19], YOLOv7 [20], YOLOv8 [21], YOLOx Tiny [22], FFCA YOLO [3], SuperYOLO [23], YOLOFusion [24], YOLOv10 [26], and YOLOv11 [27], are selected to be conducted several experiments on three publicly available RS object detection datasets (DIOR [31], HRRSD [32], and NWPU VHR-10 [33]).”
Jagan, J., Gundlapalli, P., & Samui, P. (2015) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-9474-3.ch005 · Cited by 3
Semantic annotation of satellite images via joint multi-feature learning with diversity constraint
Yao, X., Han, J., Cheng, G., Zhou, P., & Guo, L. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7730417 · Cited by 3
“With the development of modern remote sensor technologies, huge quantities of high-resolution (HR) satellite images become widely available to the public, facilitating a wide range of remote sensing applications such as land use/land cover (LULC) mapping [1-3], object detection [4-9], and regional environment monitori…”
BackgroundSelf-citationAn Optical-to-SAR Transformation Method for SAR Ship Image Augmentation
Li, K., Luan, S., & Zhou, D. X. (2020) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp50920.2020.9232097 · Cited by 3
“Here we use the open remote sensing image dataset NWPU VHR-10 [13], [14] as the input of our experiment.”
MethodZero-Watermarking Algorithm for Remote Sensing Image via BEMD and DFT
Xu, Y., Hu, K., Wang, X., & Hu, J. (2022) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2022.19183 · Cited by 3
“西安: 西安电子科技大学, 2019) [22] Cheng G, Han J W, Zhou P C, et al.”
Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases
Muhammad, F., Khan, J., Ullah, A., Ullah, F., Khan, R., Khan, I. U., ELAffendi, M. A., & Ali, G. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038748 · Cited by 3
An Open Image Resizing Framework for Remote Sensing Applications and Beyond
Occorsio, D., Ramella, G., & Themistoclakis, W. (2023) · Remote Sensing · doi:10.3390/rs15164039 · Cited by 3
A Task-Risk Consistency Object Detection Framework Based on Deep Reinforcement Learning
Wen, J., Liu, H., & Li, J. (2023) · Remote Sensing · doi:10.3390/rs15205031 · Cited by 4
Huang, W., Zhao, Y., Sun, L., Gao, L., & Chen, Y. (2023) · Remote Sensing · doi:10.3390/rs15215200 · Cited by 3
Learning to detect extreme objects for remote sensing images
He, Q., Li, M., Huo, L., & Chen, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02059-2 · Cited by 3
“The NWPU VHR-10 [3] satellite image dataset and RSOD dataset [21] were used for the experiment.”
Li, C., Ma, L., Zhang, J., Zhao, S., Wang, Z., & Jiang, R. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10452032 · Cited by 3
“We conduct a series of experiments on two public datasets of remote sensing images to illustrate the superior performance of the proposed method, including NWPU VHR-10 [13] and DIOR [14].”
MethodConfidence-Based Weakly Supervised for Aircraft Detection From Remote Sensing Image
Zhu, X., Lin, R., Zhang, Y., & Chen, X. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3386193 · Cited by 3
“In this letter, we evaluate the proposed method using weakly supervised standard benchmarks on the NPWU VHR-10 [17] and PatternNet [13] datasets.”
MethodRemote Sensing Image Rectangling With Iterative Warping Kernel Self-Correction Transformer
Qiu, L., Xie, F., Liu, C., Wang, K., Song, X., & Shi, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3441246 · Cited by 3
“Step 2: Randomly crop the remote sensing (RS) image from the validation set of NWPU VHR-10 dataset [1].”
Method
Metadata
- Authors
- Cheng, G., Han, J., Zhou, P., & Guo, L.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Han, Junwei
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 98
- Pages
- 119-132
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2005368619
- Cited by
- 1,007
- References
- 38
- Citations by year
- 2026: 115 · 2025: 152 · 2024: 141 · 2023: 111 · 2022: 105 · 2021: 105 · 2020: 87 · 2019: 64 · 2018: 41 · 2017: 44 · 2016: 26 · 2015: 16
- FWCI
- 43.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Funders
- Ministry of Education of the People's Republic of China (20136102110037)
- National Natural Science Foundation of China (61401357, 61333017, 61473231, 91120005)
- China Postdoctoral Science Foundation (2014M552491)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- geospatial object detection, linear support vector machine, spatial pattern detection, remote sensing images, image representation
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