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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]
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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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Efficient Detection in Aerial Images for Resource-Limited Satellites
Li, L., Cao, G., Liu, J., & Tong, Y. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3046739 · Cited by 10
Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification
Chen, X., Ma, M., Li, Y., & Cheng, W. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3130073 · Cited by 10
Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images
Zhou, P., Zhang, D., Cheng, G., & Han, J. (2015) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2015.13 · Cited by 10
“[9, 10] proposed a Collection of Part Detectors (COPD) method to detect multi-class geospatial objects on a public high-spatialresolution RSIs data set containing 10-class objects(1).”
MethodSelf-citationDeep sparse representations for land-use scene classification in remote sensing images
Hu, F., Xia, G.-S., & Zhang, L. (2016) · International Conference on Signal Processing · doi:10.1109/icsp.2016.7877822 · Cited by 10
Object detection in optical remote sensing images by integrating object-to-object relationships
Tian, Z., Zhan, R.-H., Wang, W., He, Z., Zhang, J., & Zhuang, Z. (2020) · Remote Sensing Letters · doi:10.1080/2150704x.2020.1722330 · Cited by 11
“Northwestern Polytechnical University (NWPU) very high resolution (VHR) 10 dataset (Cheng et al. 2014) is employed to evaluate the proposed method.”
MethodRemote Sensing Imagery Object Detection Model Compression via Tucker Decomposition
Huyan, L., Li, Y., Jiang, D., Zhang, Y., Zhou, Q., Li, B., Wei, J., Liu, J., Zhang, Y., Wang, P., et al. (2023) · Mathematics · doi:10.3390/math11040856 · Cited by 9
A Light-Weight Deep Learning Model for Remote Sensing Image Classification
Pham, L. D., Le, C., Ngo, D. T., Nguyen, A. Q., Lampert, J., Schindler, A., & McLoughlin, I. V. (2023) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa58351.2023.10279679 · Cited by 9
“Subsequently, more challenging RSIC datasets have been published, such as NWPU VHR-10 (2014) [5], SAT6 (2015) [6], SIRI-WHU (2015) [7], AID (2017) [8], OPTIMAL (2018) [9], NWPU-RESISC45 (2017) [10], etc.”
Scale Information Enhancement for Few-Shot Object Detection on Remote Sensing Images
Yang, Z., Zhang, Y., Zheng, J., Yu, Z., & Zheng, B. (2023) · Remote Sensing · doi:10.3390/rs15225372 · Cited by 9
Feature Pyramid Full Granularity Attention Network for Object Detection in Remote Sensing Imagery
Liu, C., Qi, X. Y., Yin, H., Song, B., Li, K., & Shen, F. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5609-4_26 · Cited by 9
Object detection in aerial images using DOTA dataset: A survey
Chen, Z., Wang, H., Wu, X., Wang, J., Lin, X., Wang, C., Gao, K., Chapman, M., & Li, D. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104208 · Cited by 11
Binary Lightweight Neural Networks for Arbitrary Scale Super-Resolution of Remote Sensing Images
Wang, Y., Zhang, H., Zeng, X., Wang, B., Li, W., & Ding, W. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3529696 · Cited by 9
“Besides, the NWPU VHR-10 [68] is chosen as the dataset, which includes 650 annotated images and 150 background annotated images.”
Global-Integrated and Drift-Rectified Imprinting for Few-Shot Remote Sensing Object Detection
Yan, B., Cheng, G., Lang, C., Huang, Z., & Han, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3546062 · Cited by 10
“The proposed method is evaluated on two standard FSOD benchmarks, i.e., DIOR [60] and NWPU VHR-10.v2 [61].”
Self-citationTang, Y., Zhao, Z., & Huang, O. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111732 · Cited by 11
A two-way dense feature pyramid networks for object detection of remote sensing images
Li, H., Ma, H., Che, Y., & Yang, Z. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01916-4 · Cited by 9
“The NWPUVHR-10 dataset contains two sub-datasets: one is the positive sample dataset of 650 images, and the other is the negative sample dataset of 150 images.”
MethodInfluentialLiang, B., Su, J., Feng, K., Liu, Y., Zhang, D., & Hou, W. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3348097 · Cited by 9
“However, directly transferring these models to remote sensing image datasets focusing on small objects and intricate backgrounds, such as the DIOR [15], RSOD [16], and NWPU VHR-10 [17] datasets, the detection accuracy is significantly degraded and does not meet satisfactory standards.”
BackgroundInfluentialDuan, S., Cheng, P., Wang, Z., Wang, Z., Chen, K., Sun, X., & Fu, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3353192 · Cited by 9
“Since the current publicly available RS object detection datasets [59], [60], [61], [62], [63] only reflect single-platform observations, there is a lack of corresponding datasets for mul-tiplatform collaborative inference.”
BackgroundMethodTian, Y., & Shi, X. (2024) · Agriculture · doi:10.3390/agriculture14040526 · Cited by 9
Yao, X., Feng, X., Cheng, G., Han, J., & Guo, L. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8899285 · Cited by 9
“To quantitatively evaluate the proposed method, we compared it with four state-of-the-art methods, in which the bag of visual words (BoVW)based method [25], the fisher discrimination dictionary learning (FDDL) method [7], the collection of part detectors (COPD) method [22], and rotationinvariant histogram of gradients…”
MethodSelf-citationTian, Y., Liu, J., Zhu, S., Xu, F., Bai, G., & Liu, C. (2022) · Remote Sensing · doi:10.3390/rs14143347 · Cited by 9
Detection of River Floating Debris in UAV Images Based on Improved YOLOv5
Huang, J., Jiang, X., & Jin, G. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892392 · Cited by 9
“Commonly used aerial image datasets include DOTA [27], UCAS-AOD [28], NWPU VHR-10 [29], [30], [31] and RSOD [32], [33].”
MethodHe, C., Tu, M., Liu, X., Xiong, D., & Liao, M. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6110336 · Cited by 9
Ju, L., Yu, H., Xiang, Q., Hu, W., & Xu, X. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054312 · Cited by 8
“The Geodetector model is a spatial analysis model that detects spatial heterogeneity and reveals the relationship between geographical attributes and their underlying influences [21].”
BackgroundShi, W., Zhang, S., & Zhang, S. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.044863 · Cited by 10
Zhang, J., Deng, F., Wang, Y., Gong, J., Liu, Z., Liu, W., Zeng, Y., & Chen, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3469209 · Cited by 8
MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance
Su, N., Zhao, Z., Yan, Y., Wang, J., Lu, W., Cui, H., Qu, Y., Feng, S., & Zhao, C. (2024) · Remote Sensing · doi:10.3390/rs16234485 · Cited by 10
Implications of Artificial Intelligence for Assessing the Built Environment
Moradi, F., & Biloria, N. M. (2025) · Journal of Urban Technology · doi:10.1080/10630732.2025.2468142 · Cited by 9
“Cheng et al. (2014) developed a rotation-invariant model for geographic image classification as well as multi-class geospatial object detection.”
A Semantic-Guided Framework for Few-Shot Remote Sensing Object Detection
Sun, C., Jia, Y., Han, H., Li, Q., & Wang, Q. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3592484 · Cited by 10
“With the advancement of Earth observation technology, large-scale remote sensing datasets [4], [5] have been established, which drives the advancement in deep learning-based remote sensing object detection.”
InfluentialEnhancing remote sensing object detection via selective-perspective-class integration
Wang, X., Lian, P., Li, X., Yi, H., & KinTak, U. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113416 · Cited by 8
Semantic contrastive learning via VLM for few-shot remote sensing object detection
Yan, B., Lang, C., & Cheng, G. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113465 · Cited by 8
Self-citationRescaling-Assisted Super-Resolution for Medium-Low Resolution Remote Sensing Ship Detection
Zou, H., He, S., Cao, X., Li, S., Wei, J., Liu, S., & Liu, J. (2022) · Remote Sensing · doi:10.3390/rs14112566 · Cited by 8
“In this section, we conduct extensive ablation studies on the HRSC2016 [29], DOTA [30] and NWPU VHR-10 [31] datasets to validate the effectiveness of our proposed RASR for both image SR and ship detection tasks.”
MethodInfluentialTian, S., Cao, L., Kang, L., Xing, X., Tian, J., Du, K., Sun, K., Fan, C., Fu, Y., & Zhang, Y. (2022) · Remote Sensing · doi:10.3390/rs14194951 · Cited by 8
Transfer Learning for On-Orbit Ship Segmentation
Fanizza, V., Rijlaarsdam, D., González, P. T. T., & Espinosa-Aranda, J. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_2 · Cited by 8
Guo, X., Hou, B., Yang, C., Ma, S., Ren, B., Wang, S., & Jiao, L. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103244 · Cited by 9
Arbitrary Oriented Few-Shot Object Detection in Remote Sensing Images
Wu, W., Jiang, C., Yang, L., Wang, W., Chen, Q., Zhang, J., Yang, H., & Chen, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3461165 · Cited by 8
“To accommodate the oriented object detection, we performed experiments using the rotated annotations of two datasets, NWPU [33] and DIOR [34], denoted as NWPU-R and DIOR-R, respectively.”
BackgroundMethodWan, L., Liu, N., Guo, Y., Huo, H., & Fang, T. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.016017 · Cited by 8
“Histogram-of-oriented-gradient (HOG)(15) feature pyramids were also adopted to train part detectors for finding distinctive visual elements in images to be used as image representations.(16) Mekhalfi et al.”
MethodZhao, K., Ren, X., Kong, Z., & Liu, M. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.3.033026 · Cited by 8
“To deal with the problem of object rotation variations, Cheng et al.15 proposed an effective approach to learn a rotationinvariant CNNs model for advancing the performance of *Address all correspondence to Xiaoxi Ren, E-mail: happyrxx@hnu.edu.cn 1017-9909/2019/$25.00 © 2019 SPIE and IS&T Journal of Electronic Imaging…”
BackgroundInfluentialZou, H., He, S., Wang, Y., Li, R., Cheng, F., & Cao, X. (2022) · Remote Sensing Letters · doi:10.1080/2150704x.2022.2033343 · Cited by 8
“Our customized NWPU VHR-10 dataset contains 249 images for training and 52 images for validation.”
BackgroundInfluentialPistonNet: Object Separating From Background by Attention for Weakly Supervised Ship Detection
Yang, Y. H., Pan, Z., Hu, Y., & Ding, C. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3184637 · Cited by 8
A Method of the Coverage Ratio of Street Trees Based on Deep Learning.
Wen, H., Cao, L., & Xu, S. Y. (2022) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2022.07.003 · Cited by 8
Zhou, Y. Z., Xia, L., Zhao, J., Yao, R., & Liu, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15608-2 · Cited by 11
“These ten classes of objects are airplane, ship, storage tank, baseballdiamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle [18].”
Li, S., Zhang, Z., & Li, B. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/960/1/012025 · Cited by 8
Ghorbani, F., Ebadi, H., & Sedaghat, A. (2019) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-019-00944-4 · Cited by 8
Cheng, B., Li, Z., Wu, Q., Li, B. H., Yang, H., Qing, L., & Qi, B. (2019) · IEEE Access · doi:10.1109/access.2019.2943346 · Cited by 10
“And collection of part detectors [66] is representative state-of-the-art SVM-based method.”
BackgroundMethodObject-based feature extraction for hyperspectral data using firefly algorithm
Shahdoosti, H. R., & Tabatabaei, Z. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01038-w · Cited by 8
Which CAM is Better for Extracting Geographic Objects? A Perspective From Principles and Experiments
Su, Q., Zhang, X., Xiao, P., Li, Z., & Wang, W. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3188493 · Cited by 8
Skip-Encoder and Skip-Decoder for Detection Transformer in Optical Remote Sensing
Yang, F., Chen, G., & Duan, J. (2024) · Remote Sensing · doi:10.3390/rs16162884 · Cited by 8
Efficient Remote Sensing Image Target Detection Network With Shape-Location Awareness Enhancements
Fan, F., Zhang, M., Yu, D., Li, J., & Liu, G. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3444920 · Cited by 7
“Also, our proposed model will be validated on the remote sensing dataset DIOR [19], NWPU VHR-10 [20], and RSOD [21].”
MethodVETRA: A Dataset for Vehicle Tracking in Aerial Imagery – New Challenges for Multi-Object Tracking
Hellekes, J., Muhlhaus, M. S., Bahmanyar, R., Azimi, S. M., & Kurz, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_4 · Cited by 7
“AI-TOD-v2 [45], DIOR [24], COWC [33], UCAS-AOD [56], and NWPU VHR-10 [11].”
MSA-YOLOv5: An Improved Light-Weight YOLOv5 Model for Small Object Detection
Park, J.-C., Kim, M., & Kim, G.-W. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10827062 · Cited by 8
“Cheng et al. (2014) [7] proposed a method using a collection of part detectors (COPD) to detect various classes of geospatial objects and classify geographic images.”
A lightweight hybrid neural network for remote sensing image super-resolution reconstruction
Zhang, Q., Zhu, F., Zhu, B., Li, P., & Han, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111438 · Cited by 7
Pan, L., Cao, X., Zou, H., Chen, H., He, S., Zhang, Y., Liu, X., Li, J., & Chen, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111993 · Cited by 9
PSODNet: Pretrained Scene-Aware Object Detection for Optical Remote Sensing Imagery
Liu, H., He, D., Zhao, Y., Shi, Q., & Liu, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3615623 · Cited by 7
“A. Datasets 1) NWPU VHR-10: NWPU VHR10 [65] includes ten categories and was proposed by Northwestern Polytechnical University in Xi’an and contains 1172 images, each 400 × 400 pixels, with 3651 object instances.”
Liu, Q., Yu, H., Zhang, P., Geng, T., Yuan, X., Ji, B., Zhu, S., & Ma, R. (2025) · Remote Sensing · doi:10.3390/rs17243996 · Cited by 7
Cross-scale adaptive transformer with hierarchical feature synergy for aerial small object detection
Zhang, W., & Liao, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112822 · Cited by 7
Shi, Y., Yang, R., Yin, C., Lu, Y., Huang, B., Tao, Y., & Zhong, Y. (2026) · Remote Sensing · doi:10.3390/rs18020266 · Cited by 7
Ye, P., Liu, G., & Huang, Y. (2022) · Applied Sciences · doi:10.3390/app12126000 · Cited by 8
Zhao, W., Liu, Y., Liu, P., Wu, H., & Dong, Y. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103648 · Cited by 7
EB-Net: an efficient balanced network for accuracy and speed of remote sensing detection
Zhang, D., Wang, D., Yu, C., Hao, X., & Liang, L. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2343137 · Cited by 7
“To verify that the proposed DSFormer attention mechanism is more suitable for light-weight remote sensing, ablation study is conducted to compare DSFormer with the several existing well-performing attention mechanisms (Liu et al. 2021; Tu et al. 2022; Woo et al. 2018; Zhang and Yang 2021) on the NWPU VHR-10 dataset.”
MethodInfluentialRemote Sensing Image Harmonization Method for Fine-Grained Ship Classification
Zhang, J., Zhong, Z., Wei, X., Wu, X., & Li, Y. (2024) · Remote Sensing · doi:10.3390/rs16122192 · Cited by 7
“The image conversion network and the remote sensing ship image harmonization algorithm advocated in this article are benchmarked against other conversion techniques rooted in deep learning, to discern the effectiveness of this algorithm.”
MethodBalanced Orthogonal Subspace Separation Detector for Few-Shot Object Detection in Aerial Imagery
Jiang, H., Wang, Q., Feng, J., Zhang, G., & Yin, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3423305 · Cited by 7
“1) NWPU VHR-10: The NWPU VHR-10 dataset [75] contains 800 high-resolution satellite images cropped from Google Earth and Vaihingen dataset [76].”
Object Detection for Remote Sensing Based on the Enhanced YOLOv8 With WBiFPN
Shen, L., Lang, B., & Song, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3487492 · Cited by 7
“The NWPU VHR-10 dataset [54] contains 650 annotated images representing types of ground objects, along with 150 background images.”
BackgroundZhang, H., Zhang, Y., Wang, D., & Ma, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3493886 · Cited by 7
“In addition, common remote sensing ODs are almost always single-image-based studies using publicly available datasets, such as DOTA [12], DIOR [13], and NWPU VHR [14], and there are almost no OD-based BCD studies based on two images.”
Chen, J., Guo, Y., Qin, D., Zhu, J., Gou, Z., & Sun, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3520715 · Cited by 7
Context-Driven Automatic Target Detection With Cross-Modality Real-Synthetic Image Merging
Geng, Z., Zhang, S., Xu, C., Zhou, H., Li, W., Yu, X., Zhu, D., & Zhang, G. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3531788 · Cited by 7
“…the widely used open-source datasets in the field of context analysis for remote sensing images, which include the UC-Merced [63], WHU-RS19 [64], NWPU VHR-10 [65], AID [66], RSOD [67], OPTIMAL-31 [68], DOTA [69], and DIOR [70], feature more object categories, and higher intraclass diversity.”
Online Target Recognition for Time-Sensitive Space Information Networks
Huo, C., Zhou, Z., Ding, K., & Pan, C. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2017.2655448 · Cited by 7
“, kNN [26], SVM [27], [28], latent SVM [29], [30], Adaboost [31], CRF [32], neural network [33], [34], Random Forest [35]), knowledge and inference(context knowledge [36], Dempster-Shafer evidential inference [37]), etc.”
A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN
Dong, R., Yang, R., Tang, Y.-W., & Shi, Z. (2018) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce.2018.00040 · Cited by 7
“This dataset contains totally 800 very-high-resolution (VHR) remote sensing images that were cropped from Google Earth and Vaihingen dataset and then manually annotated by experts [12-14].”
MethodNoise Resistant Focal Loss for Object Detection
Hu, Z., Gao, K., Zhang, X., & Dou, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60639-8_10 · Cited by 7
Single image mixed dehazing method based on numerical iterative model and DehazeNet
Jiao, W., Jia, X., Liu, Y., Jiang, Q., & Sun, Z. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254664 · Cited by 7
“Our method has the best entropy scores in the NWPU VHR-10 [42, 43], RSOD [45, 46] and DOTA datasets [47].”
BackgroundMethodResultInfluentialGuo, X., Hou, B., Wu, Z., Ren, B., Wang, S., & Jiao, L. (2022) · Remote Sensing · doi:10.3390/rs14133042 · Cited by 7
“The NWPU VHR-10 dataset [38], which is a publicly available 10-class geospatial object detection dataset, was used for testing.”
MethodLiu, L., Cheng, L., Ji, C., Jing, M., Li, N., & Chen, H. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3196640 · Cited by 7
“On the one hand, understanding the vast and complex remote sensing images [10], [11], [12], [13] is critical, while on the other hand, it provides a unique opportunity to extract runway heading information data rapidly and automatically.”
BackgroundChen, C., Kong, H., & Wu, B. (2023) · Electronic Research Archive · doi:10.3934/era.2023066 · Cited by 7
Shi, Y., Huang, Z., Huang, Z., Hua, X., Hong, H., & Li, L. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104722 · Cited by 7
Clark, C. N., Bedada, A., Huff, B., Lita, B., Newbury, A. B., & Pacifici, F. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3284309 · Cited by 7
CODet: Component Object Detector Extracting Structural Features Based on Target Characteristics
Zhu, Z., Sun, X., Diao, W., Chen, K., He, Q., Xu, G., & Fu, K. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3281331 · Cited by 7
“The task of feature modeling and extraction for rigid objects (vehicles, aircraft, ships, and so on) has developed rapidly in recent years [11], [12], [13], [14], [15], [16].”
BackgroundA comprehensive review of optical remote-sensing image object detection datasets
Yiqin, Y., Li, L., Yao, X., Li, L., Feng, X., Cheng, G., & Han, J. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20233457 · Cited by 7
Self-citationYao, F., Wang, S., Li, R., Chen, L., Gao, F., & Dong, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.070 · Cited by 7
Remote sensing image target detection based on a multi-scale deep feature fusion network
Xinnan, F., Wei, Y., Pengfei, S., & ZHANG, X. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210170 · Cited by 7
Feature Enhanced Centernet for Object Detection in Remote Sensing Images
Zhang, T., Wang, G., Zhuang, Y., Chen, H., Shi, H., & Chen, L. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323116 · Cited by 6
Xu, Y., He, W. P., Zhang, G., Qi, Q., Chen, S., Wu, J., & Tu, B. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3564634 · Cited by 6
“Extensive experiments on challenging remote sensing datasets, including DIOR [12], NWPU VHR-10 [13], RSOD [14], and AI-TOD [15] (many instances meet the criteria for small weak object detection [16]), clearly demonstrate the superiority of PGCFNet.”
MCRS-YOLO: Multi-Aggregation Cross-Scale Feature Fusion Object Detector for Remote Sensing Images
Liu, L., & Li, J. (2025) · Remote Sensing · doi:10.3390/rs17132204 · Cited by 8
An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7
Dong, C., & Jiang, X. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.044735 · Cited by 6
Peng, L., Wei, T., Chen, X., Chen, X., Sun, R., Wan, L., Chen, J., & Zhu, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3502461 · Cited by 10
“Comparedtonaturalimages,remotesensingimagescenes presentedmoreorientationandscalevariations[32],[33].”
Fanfan, 刘. L., Chengmei, 朱. Z., Nana, 赵. Z., & Jinghua, 吴. W. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop241203 · Cited by 6
Zhao, J., Duan, J., Qiao, L., He, T., Shi, X., & Yan, B. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07003-5 · Cited by 6
“The specific information of the dataset is as follows: NWPU VHR-10 Dataset: This dataset was proposed by Chen et al. [51] from the Northwestern Polytechnical University in China.”
LW-ODF: A Light-Weight Object Detection Framework for Optical Remote Sensing Imagery
Wu, X., Hong, D., Ghamisi, P., Li, W., & Tao, R. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898673 · Cited by 6
“Similarly, we use a VGG-16 network as the pretrained network and perform the fine-tuning on the NWPU VHR-10 dataset [11] for further feature extraction .”
MethodOn the Evaluation of CNN Models in Remote-Sensing Scene Classification Domain
Sen, O., & Keleş, H. Y. (2020) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-020-00129-6 · Cited by 6
“RSSC has a wide range of applications such as land-use and land-cover detection (Gómez-Chova et al. 2015), geospatial object classification (Cheng et al. 2014a), natural hazard detection (Martha et al. 2011), vegetation mapping, urban planning (Li and Shao 2013), and so on.”
BackgroundContext Residual Attention Network for Remote Sensing Scene Classification
Wang, Y., Hu, Y., Xu, Y., Jiao, P., Zhang, X., & Cui, H. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3117265 · Cited by 6
A Framework of Maximum Feature Exploration Oriented Remote Sensing Object Detection
Li, Y., Xing, Y., Wang, Z., Xiao, T., Song, Q., Li, W., & Wang, J. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3228689 · Cited by 6
“The quantitative verification experiments on NWPU VHR-10 are summarized in Table IV.”
InfluentialAhuja, S., Patil, S., & Bhangale, U. M. (2023) · Annals of GIS · doi:10.1080/19475683.2023.2181394 · Cited by 6
“The results of YOLOv5 object detection on images from the NWPU VHR-10 dataset are shown in Figure 10, where objects are labeled and their position is also located.”
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification
Le, C., Pham, L. D., Nvn, N., Nguyen, T., & Trang, L. H. (2023) · International Conference on Intelligent Information Technology (ICIIT) · doi:10.1145/3591569.3591601 · Cited by 6
“In the next years, various remote sensing image datasets were published such as WHU-RS19 [48] in 2012, NWPU VHR-10 [5], SAT6 [1] and RSSCN7 [65] in 2015, SIRI-WHU [59] in 2016, AID [46] and NWPU-RESISC45 [3] in 2017, and OPTIMAL [42] in 2018.”
MethodGong, X., & Liu, D. (2024) · Remote Sensing Letters · doi:10.1080/2150704x.2024.2334194 · Cited by 6
A New Instance Segmentation Model for High-Resolution Remote Sensing Images Based on Edge Processing
Zhang, X., Shen, J., Hu, H., & Yang, H. (2024) · Mathematics · doi:10.3390/math12182905 · Cited by 6
Luo, C., Wang, J., Feng, G., Xu, S., & Wang, S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042613 · Cited by 7
Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion
Zhou, L., Yan, H., Chang, Z., Rao, X., Li, Y., Yang, W., Tian, J., Fan, M., & Zuo, X. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/7618828 · Cited by 6
Multiscale object detection in remote sensing image by combining data fusion and feature selection
Dengda, Q., Li, W., He, P., Yi, Z., Guo, Y., & Jie, C. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20221249 · Cited by 5
Supervised Adaptive-RPN Network for Object Detection in Remote Sensing Images
Tang, X., Zhang, H., Ma, J., Zhang, X., & Jiao, L. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323998 · Cited by 6
“To further study our model, we compare it with several methods, including Exemplar-SVMs [11], the collection of part detectors (COPD) [10], rotation-invariant CNN (RICNN) [12], HyperNet [13], and the rotation-insensitive and context-augmented (RICA) [14] method.”
MethodYang, X., Xue, S., Li, L., Li, S., Fang, Y., Zhang, X., & Huang, X. (2025) · Remote Sensing · doi:10.3390/rs17132213 · Cited by 6
Dynamic Snake Convolution Neural Network for Enhanced Image Super-Resolution
Xin, W., Wu, Z., Zhu, Q., Bi, T., Li, B., & Tian, C. (2025) · Mathematics · doi:10.3390/math13152457 · Cited by 6
Dobrzycki, A. D., & Bernardos, A. M. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07142-z · Cited by 5
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Huang, S., He, S., Qin, H., & Wen, B. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01167 · Cited by 5
“To evaluate the effectiveness of our method, we conduct cross-dataset evaluation on 4 aerial instance segmentation datasets, i.e ., NWPU-VHR-10 [7, 44], SOTA [48], FAST [48], and SIOR [48].”
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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