Cited by 1,007Remote-Sensing Image ClassificationAdvanced Image and Video Retrieval TechniquesAdvanced Neural Network Applications

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Multi-class geospatial object detection and geographic image classification based on collection of part detectors

Cheng, Gong, Han, Junwei, Zhou, Peicheng, Guo, Lei·2014·ISPRS Journal of Photogrammetry and Remote Sensing

DOI 10.1016/j.isprsjprs.2014.10.002

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Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
A Bayesian Hierarchical Model for Learning Natural Scene Categories
Fei-Fei, L., & Perona, P. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.16 · Cited by 3,665
Bag-of-visual-words and spatial extensions for land-use classification
Yang, Y., & Newsam, S. D. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869829 · Cited by 3,183
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
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865

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Enhanced Remote Sensing Instance Segmentation with Feature Fusion
Tao, Z., Li, Y., Hao, X., Wu, B., Shang, R., & Jiao, L. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10640731 · Cited by 3
“To comprehensively verify the effectiveness of the FEA-Net proposed in this article, Two datasets are used in our experiments, including the NWPU VHR-10 dataset[5] and the iSAID dataset[6].”
BackgroundMethod
AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training
Bor, N., Akman, N. P., & Berkol, A. (2024) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/jarnas.1500916 · Cited by 3
OMAD-6: Advancing Offshore Mariculture Monitoring with a Comprehensive Six-Type Dataset and Performance Benchmark
Mo, Z., Liang, Y., Chen, Y., Shen, Y., Xu, M., Wang, Z., & Zhang, Q. (2024) · Remote Sensing · doi:10.3390/rs16234522 · Cited by 3
Few-Shot Object Detection in Satellite Imagery with Feature Fusion Pyramid and Adaptive Region Proposal Networks
Feng, T., Li, W., Tan, Z., Zhang, L., Yuan, X., & Chu, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890247 · Cited by 3
“A. Dataset a) NWPU VHR-10 Dataset: The NWPU VHR-10 Dataset [15] is a well-known dataset designed for object detection in high-resolution aerial imagery.”
Multi-Granularity Domain-Adaptive Teacher for Unsupervised Remote Sensing Object Detection
Fang, F., Kang, J.-N., Li, S., Tian, P., Liu, Y., Luo, C., & Zhou, S. (2025) · Remote Sensing · doi:10.3390/rs17101743 · Cited by 3
Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach
Adorni, P., Pham, M., May, S., & Lefèvre, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00292 · Cited by 4
Unsupervised Remote Sensing Image Super-Resolution Method Based on Adaptive Domain Distance Measurement Network
Hou, Y., & Zhang, J. (2020) · International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) · doi:10.1109/aemcse50948.2020.00062 · Cited by 3
Vector encoded bounding box regression for detecting remote-sensing objects with anchor-free methods
Liu, X., Di, X., Wu, J., & Huang, J. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1811918 · Cited by 3
A generative adversarial network based image augmentation method for ship segmentation in SAR images
Zhao, K., Zhou, Y., Chen, X., & Zhang, H. (2020) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic49862.2020.9338897 · Cited by 3
“In this paper, the aerial images are selected from the DOTA [7], [8] and the NWPU VHR-10 dataset [9]–[11].”
Method
X-ray security check image recognition based on attention mechanism
Wu, X., & Liu, C. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2216/1/012104 · Cited by 3
Two-Stage Object Detection Based on Deep Pruning for Remote Sensing Image
Wang, S., Wang, M., Zhao, X. T., & Liu, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99365-2_12 · Cited by 3
An End-to-End Practice of Remote Sensing Object Detection with NVIDIA Embedded System
Huang, J., Su, H., Liu, X., Li, W., Cai, Y., & Wang, L. (2021) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd51990.2021.9458957 · Cited by 3
Active Contour Building Segmentation Model based on Convolution Neural Network
Liu, M., Liu, P., Song, B., Zhang, Y., & Zhang, L. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1004/1/012015 · Cited by 3
“Data and Experiment In the experiment, we used the Vaihingen buildings dataset, which consists of 168 buildings extracted from the "2Dsemanticlabelingcontest" training sets[9][10][11].”
Method
Fine-tuning Faster-RCNN tailored to Feature Reweighting for Few-shot Object Detection
Zhu, D., Guo, H., Li, T., & Meng, Z. (2022) · International Conference on Control and Computer Vision (ICCCV) · doi:10.1145/3561613.3561621 · Cited by 3
“In this section, we evaluate the performance of our model on the public remote sensing image dataset NWPU VHR-10, which is a 10-class very-high-resolution (VHR) geospatial object detection dataset from NorthWestern Polytechnical University (NWPU)[3].”
Method
Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan AfricaPreprint
Omeiza, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00340 · Cited by 3
“…processes (Martha et al., 2011; Cheng et al., 2013; Stumpf & Kerle, 2011), LULC determination (Wu et al., 2016), geospatial object detection (Cheng et al., 2014), geographic image retrieval (Wang et al., 2016; Li et al., 2016), vegetation mapping (Kim et al., 2009; Li & Shao, 2013),…”
Scale-adaptive salience supervision and dynamic token filtering for small object detection in remote sensing images
Zhang, Q., Yi, S., Shao, M., & Hong, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad90ff · Cited by 2
Pretrained Detail Enhancement Framework for Remote Sensing Object Detection
Zhou, M., Zhou, Y., Yang, D., & Song, K. (2025) · IEEE Access · doi:10.1109/access.2025.3526180 · Cited by 2
Improved YOLOv8n Models for Object Detection in Remote Sensing Images
Chen, Y.-L., Hung, K.-C., Zhang, J., & Lin, L.-W. (2025) · IEEE Access · doi:10.1109/access.2025.3574856 · Cited by 4
“To further validate the generalization capability of our model, we also conducted experiments on the NWPU VHR-10 dataset [37].”
Thermal sentinel: Low-earth orbit infrared intelligent system for flying civil aircraft safety
Li, L., Zhou, X., Zhang, W., Zhong, Y., Gao, L., Yu, J., Li, X., & Chen, F. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114826 · Cited by 3
A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images
Jin, H., Song, Y., Bai, T., Sun, K., & Chen, Y. (2025) · Remote Sensing · doi:10.3390/rs17142415 · Cited by 3
PAP‐YOLOv8: Hierarchical Feature Optimization With Dual‐Stage Attention for Object Detection in Remote Sensing Scenes
Zhang, P., Liu, J., Zhang, J., & Li, X. (2025) · IET Image Processing · doi:10.1049/ipr2.70177 · Cited by 2
Rotation‐Robust Remote Sensing Image Classification Method Based on Paired Image Description of Vision‐Language Model
Liu, S., Liu, Q., Lu, S., Zhang, J., & Song, T. (2025) · Electronics Letters · doi:10.1049/ell2.70407 · Cited by 2
AttackTracer: Semantic-level adversarial attack location traceability via evidential diffusion model
Zhang, Z., Li, X., Zhang, P., Wang, K., Gao, T., & Shen, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131535 · Cited by 2
Optuna-Optimized Pythagorean Fuzzy Deep Neural Network: A Novel Framework for Uncertainty-Aware Image Classification
Karakütük, A. K., Özdemir, Ö., & Şentürk, S. (2025) · Applied Sciences · doi:10.3390/app152011097 · Cited by 2
ECHF-YOLO: a remote sensing object detection network integrating efficient convolution and high-low frequency features
Xie, H., Dong, Q., Duan, S., Wang, K., Li, Z., & Zhao, S. (2025) · Big Earth Data · doi:10.1080/20964471.2025.2575602 · Cited by 2
“Moreover, objects within these images exhibit significant scale variations and occupy a relatively small proportion of the foreground (Cheng et al., 2014).”
MFDA-YOLO: A multiscale feature fusion and dynamic alignment network for UAV small objects detection
Tian, D., Wang, X., Liu, D., & Hao, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337810 · Cited by 2
DBCFS-Net: Dynamic Blocks and Cross-Domain Feature Synergy for Remote Sensing Object Detection
Lin, Q., Zheng, Y., Sun, L., Dong, S., Wang, R., & Zhou, F. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3645796 · Cited by 2
“3) NWPU VHR-10: The NWPU VHR-10 dataset [44] is a publicly available benchmark speciﬁcally designed for object detection tasks in remote sensing imagery.”
RotCLIP: Tuning CLIP with visual adapter and textual prompts for rotation robust remote sensing image classification
Song, T., Liu, Q., Qin, A., & Liu, Y. (2025) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117407 · Cited by 2
Data-Driven Bidirectional Spatial-Adaptive Network for Weakly Supervised Object Detection in Remote Sensing Images
Wu, Z., Zheng, S., Xu, Y., Wang, L., Wei, Z., & Hua, G. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3646464 · Cited by 2
TDA-DETR: target-context guided aggregation and dynamic multi-scale feature interaction for remote sensing object detection
Lu, Z., Liu, A., Ge, F., Li, H., Yang, J., & Yu, P. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae31fc · Cited by 2
“To validate the effectiveness of the proposed TDA-DETR framework, extensive experiments were conducted on multiple publicly available remote sensing datasets, including DIOR [1], NWPU very high resolution (VHR)-10 [15], and RSOD [16].”
Remote Sensing Image Dehazing Techniques: Systematic Review, Performance Evaluation and Future Directions
Joglekar, M., & Deshpande, A. M. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10500-2 · Cited by 2
From Generic to Adaptive: Similarity-Adaptive Receptive-Field Cross DETR for Remote-Sensing Object Detection
Lin, C., Fu, Y., Xu, H., Teng, X., & Wang, T. (2026) · Remote Sensing · doi:10.3390/rs18101670 · Cited by 2
Dynamic Long-Short Range Structure Learning for Low-Illumination Remote Sensing Imagery HDR Reconstruction
Zhang, X., Lei Zhang, A., Wei, W., Ding, C., & Zhang, Y. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884521 · Cited by 2
“Dataset We select 650 images from the VHR 10[7, 8, 9] dataset as benchmark and follow [10] to degenerate each image into three different levels of IL images, denoted as I1, I2 Table 1.”
Method
DFFNet: Dynamic Feature Fusion Network for Weakly Supervised Object Detection in Remote Sensing Images
Zhu, M., Wan, S., Jin, P., & Zhang, P. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020414 · Cited by 2
“Object detection is an important task in remote sensing images (RSIs) processing, which has been widely used in many practical ﬁelds [1]–[3].”
Background
细粒度显著区域引导的遥感图像场景分类
Feiyang, 李. L., Jiangtao, 王. W., & Ziyang, 王. W. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop212616 · Cited by 2
Auto Learner of Objects Co-Occurrence Knowledge for Object Detection in Remote Sensing Images
Zheng, K., Dong, Y., Xu, W., Tan, W., & Huang, P. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3305617 · Cited by 2
Remote Sensing Image Target Detection Model Based on Improved YOLOX
Deng, W., & Gao, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240147 · Cited by 2
Light-weight ensembling of deep neural models for object recognition in remote sensing data
Revin, I., Balabanov, N. R., & Litvintseva, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.12.003 · Cited by 2
Boosting Few-Shot Remote Sensing Image Scene Classification with Language-Guided Multimodal Prompt Tuning
Bi, H., Gao, Z., Liu, K., Song, Q., & Wang, X. (2023) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci60157.2023.10403750 · Cited by 2
“The early remote sensing image scene classification methods utilize manually crafted features, such as spectral features and texture features, and then performs classification with classifiers [2].”
Method
Rotation-Lossless Non-Local Attention and Fine-Grained Feature Enhancement Based Fine-Grained Oriented Object Detection in Remote Sensing Images
Guo, W., Li, X., & Li, S. (2023) · International Conference on Control and Robotics (ICCR) · doi:10.1109/iccr60000.2023.10444857 · Cited by 2
An adversarial example attack method based on predicted bounding box adaptive deformation in optical remote sensing images
Dai, L., Wang, J., Yang, B., Chen, F., & Zhang, H. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2053 · Cited by 2
Advancements in cross-domain remote sensing scene interpretation
Zheng, X., Xiao, X., Chen, X., Lu, W., Liu, X., & Lu, X. (2024) · Journal of Image and Graphics · doi:10.11834/jig.240009 · Cited by 2
MMDAN: multiwavelet based multiscale dilated attention network for remote sensing image super-resolution
Sandana, J. S., Deivalakshmi, S., & George, S. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03371-2 · Cited by 2
An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys
Wu, C., Huang, Y., Yang, H., Yao, L., Liu, Y., & Chen, Z. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2390443 · Cited by 2
“…categories and instances, large-scale datasets such as Satellite imagery multi-vehicles dataset(SIMD) (Haroon, Shahzad, and Fraz 2020), NWPU-VHR10 (Cheng et al. 2014; Gong, Zhou, and Han 2016), Dataset of object detection in aerial images (DOTA) (Xia et al. 2018), DIOR (Li et al. 2020), xView…”
Disentangled Foreground-Semantic Adapter Network for Generalized Aerial Image Few-Shot Semantic Segmentation
Wang, Q., Yin, J., Jiang, H., Feng, J., & Zhang, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3455427 · Cited by 2
“1) NWPU VHR-10: The NWPU VHR-10 dataset [58] includes 800 high-resolution satellite images from Google Earth and the Vaihingen dataset [59], featuring 650 positive images and 150 negative images with only background.”
Method
Multi-expert contrastive learning for remote sensing long-tailed image classification
Zhang, L., Peng, L., Yang, C., & Ding, X. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2433751 · Cited by 4
“…al. 2016), urban planning (Huang, Liu, and Zhang 2015; Li and Shao 2013), natural disaster detection (Cheng et al. 2013; Gamba 2012; Martha et al. 2011), vegetation mapping (Mishra and Crews 2014; Phinn, Roelfsema, and Mumby 2012), geographic spatial object classification (Cheng et al. 2014), etc.”
A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery
Hu, J., Zhi, X., Zhang, B., Shi, T., Cui, Q., & Sun, X. (2024) · Remote Sensing · doi:10.3390/rs16244699 · Cited by 2
“In 2014, the NWPU VHR-10 remote sensing dataset [10] was published by Northwestern Polytech-nical University, which contains 10 types of typical targets including aircraft and ships, with a total of 757 aircraft instances.”
YOLO-FAD: enhancing small-object detection in high-resolution remote sensing images
Wei, X., Li, Z., & Wang, Y. (2025) · International Conference on Computer Application and Information Security (ICCAIS) · doi:10.1117/12.3061469 · Cited by 2
ORCNet: A Context-Based Network to Simultaneously Segment the Ocular Region Components
Lucio, D. R., Junior, L. A. Z., Costa, Y. M. G., & Menotti, D. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11731-3 · Cited by 2
“To assess the efﬁcacy of the proposed methodology, the researchers conducted experiments on three benchmark datasets, namely NWPU [11], RSOD [44], and DIOR [38].”
An object detection model for Ochotona curzoniae based on the dual-attention mechanism
Chen, H., & Hao, J. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01710-1 · Cited by 2
Anchor-Free Object Detection Method in Remote Sensing Image via Adaptive Multi-Scale Feature Fusion
Wang, K., Wu, W., Tie, J., Wu, X., & Fu, Y. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19673 · Cited by 2
Refining small object detection in aerial images with PF-DETR: a progressive fusion approach
Liu, J., Cao, Y., Dong, C., Zhang, X., Liang, Y., & Liang, Y. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3470 · Cited by 3
A New Remote Sensing Image Dataset for Large-Scale Remote Sensing Detection
Xie, D., Cheng, J. S., & Tao, D. (2019) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar47638.2019.9043971 · Cited by 2
“NWPU VHR-l0 [27,28] collectedthe relatively small number of 800 images in 10 categories.”
BackgroundMethod
An Improved Object Detection Algorithm Based on SSD in Remote Sensing Image
卢, 启. (2021) · Computer Science and Application · doi:10.12677/csa.2021.115163 · Cited by 2
MultiScale Object Detection in Remote Sensing Images using Deep Learning
Radhika, C. (2023) · Journal of Computer Science and Applications · doi:10.37624/jcsa/15.1.2023.11-19 · Cited by 2
Low-Quality and Multi-Target Detection in RSIs
Liu, G., Li, S., & Shao, Y. (2018) · International Conference on Information Science and Cloud Computing Companion (ISCC-C) · doi:10.22323/1.300.0026 · Cited by 2
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Gong, Z., Zhong, P., Hu, W., Liu, F., & Hui, B. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852335 · Cited by 2
“33%) [17].”
Research on Remote Sensing Image Target Detection Methods Based on Convolutional Neural Networks
Chen, H., Wang, L., Zhang, L., Li, Y., Wu, Y., & Qi, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2025/1/012068 · Cited by 2
Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network
Chen, J., Li, H., Song, L., Zhang, G., Hu, B., Wang, S., Liu, S., Li, S., Chen, T., & Liu, J. (2022) · Scientific Reports · doi:10.1038/s41598-021-03880-x · Cited by 2
Semantics reused context feature pyramid network for object detection in remote sensing images
Zhang, L., Guo, Y., & Wang, X. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.036509 · Cited by 2
“37 The details of these three datasets are described as follows: 1.”
Background
面向航摄图像目标检测的轻量级特征融合网络
Qiangqiang, 樊. F., Zaifeng, 史. S., Fanning, 孔. K., Shaoxiong, 李. L., & Jun, 肖. X. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop220859 · Cited by 2
基于跨层注意力增强的遥感小目标检测
Xingbo, 韩. H., & Fan, 李. L. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop221744 · Cited by 2
LRSNet: a high-efficiency lightweight model for object detection in remote sensing
Zhu, S., Miao, M., & Wang, Y. (2024) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.18.016502 · Cited by 2
A method for the super-resolution reconstruction of remote sensing images based on multi-scale feature fusion and an attention mechanism
Shi, J., Huang, S., & Sun, Y. (2024) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1117/12.3021583 · Cited by 2
“The dataset used was the NWPU VHR-10 [16] remote sensing dataset for model training, and the HR images were down-sampled by 2x, 3x, and 4x, respectively, using the double-3rd difference method, and the resulting LR images and the matching HR images were constructed into the data format required for…”
Method
Cascade feature selection and coarse-to-fine mechanism for nighttime multiclass vehicle detection
Duan, Y., Kuang, H., Qiu, W., Chan, L. L.-H., & Yan, H. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.3.033042 · Cited by 2
Refining the features transferred from pre-trained inception architecture for aerial scene classification
Devi, N., & Borah, B. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1629716 · Cited by 2
SOON: Specifically Optimized One-Stage Network for Object Detection in Remote Sensing Imagery
Wang, Z., Qin, H., Li, Y., Lei, J., & Xie, W. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00080 · Cited by 2
“As can be observed in Table II, in terms of the mAP over all ten object categories, SOON outperforms the COPD, the Transferred CNN, the RICNN, the Faster R-CNN and the RICAOD by 33.87%, 29.15%, 15.65%, 4.25% and 1.64%, respectively.”
MethodInfluential
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene UnderstandingPreprint
Qi, X., Zhu, P., Wang, Y., Zhang, L., Peng, J., Wu, M., Chen, J., Zhao, X., Zang, N., & Mathiopoulos, P. T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00243 · Cited by 2
“, 2016), and object detection (Cheng et al., 2014; Han et al., 2014), etc.”
Remote Sensing Image Classification Using Transfer Learning Based Convolutional Neural Networks: An Experimental OverviewPreprint
Baral, S., Aryal, J., & Sharma, N. (2023) · doi:10.36227/techrxiv.22581457.v1 · Cited by 2
Remote and Urban Environmental Area Sensing, Connectivity Issues, and Solutions Based on Emerging Technologies
Abinaya, M., Vadivu, G., Balasubramaniam, S., & Sundaravadivazhagan, B. (2025) · doi:10.1002/9781394310890.ch2 · Cited by 2
Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement
Cheng, B., Li, Z., Li, H., Ding, Z., & Qin, T. (2021) · Remote Sensing · doi:10.3390/rs13245065 · Cited by 1
Metric-based Few-shot Classification in Remote Sensing Image
Zhang, M., Chen, J., Wang, G., Wang, M., & Sun, K. (2022) · Artificial Intelligence Advances · doi:10.30564/aia.v4i1.4124 · Cited by 1
“Based on the remote sensing image dataset of DOTA [19], NWPU VHR-10 [20-22], RSD-GOD [23] and some remote sensing images from Gaofen-2 satellite, this paper prepared a remote sensing dataset of few-shot classification, which is called RSD-FSC, for target recognition.”
Method
Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites
Li, L., Cao, G., Liu, J., & Cai, X. (2023) · Sensors · doi:10.3390/s23239479 · Cited by 1
The Synthetic Aperture Radar-based Image Augmentation Using Generative Adversarial Network
Zhao, K., Xiong, W., & Yu, X. (2023) · International Conference on Systems, Control and Communications (ICSCC) · doi:10.1145/3634865.3634867 · Cited by 1
“In the present paper, remote sensing images were selected from the DOTA [12], [13] dataset�NWPU VHR-10 [14] dataset and other public datasets.”
Method
Enabling Near-Zero Cost Object Detection in Remote Sensing Imagery via Progressive Self-Training
Zhang, X., Jiang, X., Hu, Q., Luo, H., Zhong, S., Tang, L., Peng, J., & Jianping Fan, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3415002 · Cited by 1
Ship Kelvin Wake Velocity Inversion Method Based on KelvinPointNet in Polarization Images
Chen, H., Wang, D., Zheng, Z., Li, Z., Wang, X., Liu, Y., Zhao, J., Lin, H., Ni, K., & Zhou, Q. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3542216 · Cited by 1
“Cheng et al. [53], [54] and Cheng and Han [55] produced the NWPU VHR-10 dataset, which consists of 800 images across ten object categories, including airplanes and ships.”
Remote sensing image super-resolution reconstruction algorithm based on improved anchored neighborhood regression
Ge, W., & Fan, C. (2025) · International Conference on Big Data, Computational Intelligence, and Applications (BDCIA) · doi:10.1117/12.3059260 · Cited by 1
“NWPUVHR-10 is a dataset established by Northwestern Polytechnical University (NWPU) [13-15] for remote sensing scene classification.”
Click Prompt Learning With Feature Encoding for Segmentation of Remote Sensing Images
Song, B., Liu, P., Li, J., Zhao, L., Chen, L., Xu, M., Zeng, Y., & Liu, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3534399 · Cited by 1
“NWPU [68] is an object detection dataset that later introduced an instance segmentation version [69], sourced from aerial sensors, primarily comprising optical image (RGB three bands) with a few panchromatic images.”
LOSA: Learnable Online Style Adaptation for Test-Time Domain Adaptive Object Detection
Liu, W., Luo, B., Liu, J., Nie, H., & Su, X. B. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3576444 · Cited by 1
MFVL-YOLO: small object detection algorithm for aerial remote sensing images
Wang, F., jin, J., Xiao, C., Wang, C., & Neha, N. (2025) · Physica Scripta · doi:10.1088/1402-4896/ade1b6 · Cited by 1
RL-ISegNet: Refining Instance Segmentation for Remote Sensing Imagery via Iterative Reward Maximization With Limited Samples
Liu, Y., Zhang, T., Huang, Y., Wang, P., & Wang, G. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3598943 · Cited by 1
“Restrictions apply. two datasets, including the WHU building extraction dataset [76] and the NWPU VHR-10 dataset [77].”
MVPOD: A Dataset and Benchmark for Multi-Vertical-Perspective Object Detection in Multi-Platform Remote Sensing Images
Jin, H., Chen, J., Zhang, Y., Su, H., & Wang, B. (2025) · Remote Sensing · doi:10.3390/rs17173029 · Cited by 1
Bridging the Modality Gap: Training-Free Adaptation of Vision-Language Models for Remote Sensing via Visual Prototypes
Barbier, C., Abeloos, B., & Herbin, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00288 · Cited by 1
“Datasets We evaluate the training-free few-shot object detection approach on two widely used remote sensing benchmarks: DIOR [8] and NWPU-VHR10v2 [4].”
CGNet: Remote Sensing Instance Segmentation Method Using Contrastive Language–Image Pretraining and Gated Recurrent Units
Zhang, H., Zhao, T. Q., Chen, Z., Liu, T., Xu, X., Leng, J., & Qi, X. (2025) · Remote Sensing · doi:10.3390/rs17193305 · Cited by 1
“Among them, optical image datasets include the iSAID dataset [37], NWPU VHR-10 dataset [38], and BITCC dataset [39], while SAR image datasets comprise the HRSID dataset [40] and SSDD dataset [41].”
Few-shot object detection in remote sensing using class-decoupled GAN for pseudo-sample classification and regression
Zhang, Z., Jiangtao, W., Penglei, C., & He, C. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2565836 · Cited by 1
“By conducting experiments on the DIOR (K. Li et al. 2020) and NWPU-VHR10 v2 (Cheng et al. 2014) datasets, the effectiveness of the proposed algorithm in remote sensing applications is evaluated.”
S 2 Net: Spatial-Aligned and Semantic-Discriminative Network for Remote Sensing Object Detection
Yao, J., Chen, H., Xie, Y., Zhang, N., Yang, M., & Chen, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3622254 · Cited by 1
“Extensive experiments are conducted on three public remote sensing datasets for the assessment of S 2 Net: NWPU VHR-10 [21], DIOR [22], and RSOD [23].”
Few-shot Object Detection for Remote Sensing Images with Multi-scale Enhancement Fusion and Direction-aware Attention-RPN
Yang, F., Zhong, Y., Zhang, Z., & Zhang, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227228 · Cited by 1
“NWPU VHR-10.v2 [19].”
Improving adversarial robustness in remote sensing scene classification via non-local feature decoupling and soft self-attention masking recalibration
Wu, S., Pan, Q., Mingxin, F. U., Gao, Y., & Li, Y. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.103937 · Cited by 1
Infrared and Visible Light Image Fusion for Tidal Flats: New Challenges, Datasets, and Large Benchmark
Ren, T., Zhang, Z., Li, Y., Fang, M., Zhang, J., & Li, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3638460 · Cited by 1
“The NWPU VHR-10 dataset mitigates model bias caused by class imbalance in traditional datasets by designing a balanced set of ten land cover categories [20], [21].”
Open-vocabulary object detection for high-resolution remote sensing images
Li, H. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104566 · Cited by 3
Dual-flow context enhancement for precise instance segmentation in remote sensing imagery
Zhou, D., Yao, X., Yu, Z., & Zheng, Z. (2025) · The Visual Computer · doi:10.1007/s00371-025-04215-x · Cited by 1
A Unified Deep Architecture for Segmentation in Remote Sensing Images
Gonthina, N., & Prasad, L. V. N. (2025) · Applied Computational Intelligence and Soft Computing · doi:10.1155/acis/1918054 · Cited by 1
“However, upon application in RSI, the majority of these techniques typically experience performance degradation [1–5].”
RSD-BiasEval: A Framework for Remote Sensing Dataset Bias Analysis and Evaluation
Xie, X., He, J., Lin, Z., Li, H., Qi, J., Wang, Y., Chen, Y., & Zhang, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3626672 · Cited by 1
“…ship detection dataset released by Northwestern Polytechnical University (NWPU) in 2016, and all of its images are from Google Earth. d) NWPU VHR-10 dataset [37], [38], [39]: The NWPU VHR-10 dataset is a collection of very high resolution (VHR) RS images of ten classes, provided by NWPU in China.”
AMPE-DETR: Adaptive Multiscale Perception Enhancement DETR for Object Detection in Remote Sensing Images
Li, H., Lyu, W., Guo, Q., & Deng, Z. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3626363 · Cited by 1
Deep Learning-Based Intelligent Monitoring of Petroleum Infrastructure Using High-Resolution Remote Sensing Imagery
Zhang, N., Zhao, H., Jing, P., Gao, Y. F., Liu, S., Shen, J., Huang, S., Zeng, Q., Liu, Y., & Huang, M. (2025) · Processes · doi:10.3390/pr14010028 · Cited by 1
AMFC-DEIM: Improved DEIM With Adaptive Matching and Focal Convolution for Remote Sensing Small Object Detection
Lin, X., Li, G., Xie, J., & Zhi, Z. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3653626 · Cited by 2
“TheNWPUVHR-10[44]dataset,developedbyNorthwestern Polytechnical University, is a pivotal resource for geospatial object detection in very high-resolution (VHR) remote sensing imagery.”
A Survey on Small Object Detection in Remote Sensing Images Based on Deep Learning
Yang, L., & Wu, X. (2025) · International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) · doi:10.1109/cbase67452.2025.11335543 · Cited by 1
“4) NWPU VHR-10 [17]: 800 images, 10 classes, 3,775 instances (HBB); classic small-scale benchmark.”
Dynamic Mutual Learning for Object Detection in Aerial Imagery
Zhang, C., Yang, C., Ju, Y., Xiao, J., Jian, M., Lam, K., & Wang, Q. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3658431 · Cited by 1
“2) NWPU VHR-10 Dataset: NWPU VHR-10 [6], [79] is another popular RS object detection dataset, consisting of 800 very-high-resolution (VHR) RS images, with 650 positive samples and the remaining negatives.”
Generative adversarial network-based data augmentation for foreign object detection with small samples in railway catenary systems
Shi, T., Cai, X., Nan, X., Dai, M., & Zhao, P. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114000 · Cited by 1
Towards universal X-ray security inspection: a benchmark and stereoscopic-aware oriented prohibited item detection framework
Wang, T., Liao, K., Zhang, Z., Ma, Y., Zhi, H., Liu, A., Gong, R., & Liu, X. (2026) · Science China Information Sciences · doi:10.1007/s11432-024-4732-x · Cited by 1

Authors: Cheng, G., Han, J., Zhou, P., & Guo, L.
Affiliations: Northwestern Polytechnical University (CN)
Corresponding author: Han, Junwei
ORCID: Cheng, Gong
Han, Junwei
Zhou, Peicheng
Guo, Lei
Venue: ISPRS Journal of Photogrammetry and Remote Sensing
Publisher: Elsevier BV
Published: 2014-12
Volume: 98
Pages: 119-132
ISSN: 0924-2716 (ISSN-L)
DOI: 10.1016/j.isprsjprs.2014.10.002
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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Multi-class geospatial object detection and geographic image classification based on collection of part detectors

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