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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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Items 301–400 of 1,000
Shi, J., Jiang, Z., & Zhang, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3066539 · Cited by 28
“Apart from HRSC2016, another three main public remote sensing datasets, including ships, are NWPU VHR-10 [38], LEVIR [39], and DOTA[40].”
BackgroundSurvey of small object detection
Pan, X., Jia, N., Mu, Y., & Gao, X. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220455 · Cited by 31
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery
Hu, F., Xia, G.-S., Hu, J., Zhong, Y., & Xu, K. (2016) · Remote Sensing · doi:10.3390/rs8070555 · Cited by 26
“BOW [7] 71.86 SPM [30] 74 SCK [32] 72.52 SPCK++ [7] 77.38 SC + pooling [6] 81.67 ± 1.23 SG+ UFL [39] 82.72 ± 1.18 NFNC [71] 87.67 UFL-SC [40] 90.26 ± 1.51 COPD [12] 91.33 ± 1.11 ± 85.53 ± 1.24”
BackgroundInfluentialA Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification
Qi, K., Yang, C., Guan, Q., Wu, H., & Gong, J. (2017) · Remote Sensing · doi:10.3390/rs9090917 · Cited by 26
Multi-Scale Feature Fusion Network for Object Detection in VHR Optical Remote Sensing Images
Zhang, W., Jiao, L., Liu, X., & Liu, J. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8897842 · Cited by 26
“Performance comparison among seven methods in terms of AP and mean AP. class/methods SSCBoW FDDL COPD Transferred CNN RICNN MS-FF(0 MFLs) MS-FF(4 MFLs) airplane 0.5061 0.2915 0.6225 0.6614 0.8835 0.9232 0.9579 ship 0.5084 0.3764 0.6887 0.5693 0.7734 0.7069 0.7250 storage tank 0.3337 0.7700 0.6371 0.8432 0.8527 0.6720…”
MethodInfluentialZhao, X., Zhang, J., Tian, J., Li, Z., & Zhang, J. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1931537 · Cited by 26
“NWPU VHR-10 (Cheng et al. 2014) is a representative and publicly available object detection dataset annotated by Northwestern Polytechnical University, which is widely used in object detection task in remote sensing images.”
MethodGao, L., Wang, J., Wang, Q., Shi, W., Zheng, J., Gan, H., Lv, Z., & Qiao, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3116281 · Cited by 26
Yu, Y., Fu, L., Cheng, Y., & Ye, Q. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108350 · Cited by 26
Su, Y., Zhang, G., Mei, S., Lian, J., Wang, Y., & Wan, S. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3328889 · Cited by 26
“The traditional methods [9], [10], [11], [12], [13] are handcrafted feature-based and greatly depend on the professional knowledge, which are difficult to meet the needs of increasingly complex RS image scenes due to the limited representation capacity [14], [15].”
MethodChen, Z., Liu, T., Xu, X., Leng, J., & Chen, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3386754 · Cited by 25
“DCTC will be tested on NWPU VHR-10 dataset [43], iSAID dataset [44], SSDD [45], HRSID [46].”
MethodYang, Y., Zang, B., Song, C., Li, B., Lang, Y., Zhang, W., & Huo, P. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3417960 · Cited by 28
“Our experiments utilize two datasets: the Visdrone dataset [39] and the NWPU VHR-10 dataset [40], [41], [42].”
MethodCao, X., Wang, H., Wang, X., & Hu, B. (2024) · Electronics · doi:10.3390/electronics13173404 · Cited by 26
Feature Extraction for Patch-Based Classification of Multispectral Earth Observation Images
Georgescu, F.-A., Văduva, C., Răducanu, D., & Datcu, M. P. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2551359 · Cited by 25
“Also, [7], [8] and [9] present new techniques based on libraries of pretrained part detectors used for midlevel visual elements discovery in VHR remote sensing images.”
MethodMulti-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background
Xiao, J., Guo, H., Yao, Y., Zhang, S., Zhou, J. Y., & Jiang, Z. (2022) · Remote Sensing · doi:10.3390/rs14163969 · Cited by 25
Liu, Q., Xiang, X., Yang, Z., Hu, Y., & Hong, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3002850 · Cited by 25
“2) NWPU-Ship Data Set: For further verifying the validity of our method, we carry out experiments on another remote-sensing data set named Northwestern Polytechnical University very high-spatial resolution-10 (NWPU VHR-10) [40].”
MethodAn Efficient Feature Pyramid Network for Object Detection in Remote Sensing Imagery
Qingyun, F., Lin, Z., & Zhaokui, W. (2020) · IEEE Access · doi:10.1109/access.2020.2993998 · Cited by 24
“In experiments, we take the NWPU-VHR10 [34] to prove that our proposed ARSF network achieves the performance of SoTA detectors.”
MethodJi, C., Cheng, L., Li, N., Zeng, F., & Li, M. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3034609 · Cited by 24
“Inordertomakethetraineddeep learningmodelhavestrongerdirectionalgeneralizationability, somescholarshaveproposedrotation-invariantmethods[55], [56].”
Saliency-Guided Remote Sensing Image Super-Resolution
Liu, B., Zhao, L., Li, J., Zhao, H., Liu, W., Li, Y., Wang, Y., Chen, H., & Cao, W. (2021) · Remote Sensing · doi:10.3390/rs13245144 · Cited by 24
“NWPU VHR-10 dataset [73]: This dataset consists of 800 images divided into 10 class geospatial objects, including airplanes, ships, storage tanks, baseball diamonds, tennis courts, basketball courts, ground track fields, harbors, bridges, and vehicles.”
BackgroundMethodWeakly Supervised Object Detection for Remote Sensing Images: A Survey
Fasana, C., Pasini, S., Milani, F., & Fraternali, P. (2022) · Remote Sensing · doi:10.3390/rs14215362 · Cited by 25
Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction
AL-Alimi, D., Shao, Y., Feng, R., Al‐qaness, M. A. A., Elaziz, M. A., & Kim, S. (2019) · Remote Sensing · doi:10.3390/rs11212525 · Cited by 26
“Class RICNN R-P-F-R-CNN D-RFCN COPD PSB D-FR-CNN RDAS512 MSCNN FPN + MS SD-MS A 88.35 90.4 87.3 89.11 90.7 90.7 99.6 99.3 97.6 96.98 S 77.34 75 81.4 81.73 80.6 87.1 85.5 92 96.54 97.31 ST 85.27 44.4 63.6 97.32 80.3 70.5 89 83.2 12.95 88.45 BD 88.12 89.9 90.4 89.38 89.9 89.5 95 97.2 95.69 97.62 TC 40.83 79 81.6 73.27 7…”
BackgroundMethodInfluentialAircraft detection in remote sensing images based on deconvolution and position attention
Shi, L., Tang, Z., Wang, T., Xu, X., Liu, J., & Zhang, J. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1892858 · Cited by 24
“To illustrate the generalization capability of the DPANet, the method was applied to RSOD (Long et al. 2017; Xiao et al. 2015) and NWPU (Cheng et al. 2014; Cheng and Han 2016; Cheng, Zhou, and Han 2016).”
MethodWan, Z., Wang, S., Han, W., Wang, Y., Huang, X., Zhang, X., Chen, X., & Chen, Y. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.08.023 · Cited by 26
Gated Ladder-Shaped Feature Pyramid Network for Object Detection in Optical Remote Sensing Images
Liu, N., Çelik, T., & Li, H.-C. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3046137 · Cited by 23
TorchGeo: Deep Learning With Geospatial Data
Stewart, A. J., Robinson, C., Corley, I., Ortiz, A., Ferres, J. L., & Banerjee, A. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3707459 · Cited by 52
Few-Shot Object Detection in Remote Sensing Image Interpretation: Opportunities and Challenges
Liu, S., You, Y., Su, H., Meng, G., Yang, W., & Liu, F. (2022) · Remote Sensing · doi:10.3390/rs14184435 · Cited by 24
Yao, C., & Cheng, G. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.017 · Cited by 23
Self-citationAutomatic detection of Martian dark slope streaks by machine learning using HiRISE images
Wang, Y., Di, K., Xin, X., & Wan, W. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.04.014 · Cited by 23
“In this way, the features for describing a window containing a DSS as positive samples are indistinguishable enough such that an effective classification is not achievable (Cheng et al., 2014).”
BackgroundA Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery
Xie, W., Qin, H., Li, Y., Wang, Z., & Lei, J. (2019) · Remote Sensing · doi:10.3390/rs11111376 · Cited by 23
“The other dataset adopted in this work to evaluate the performance of NEOON is the NWPU VHR-10 dataset [45,58,59] consisting of a positive image set including 650 images and a negative image set including 150 images.”
BackgroundMethodInfluentialZhang, Z., Jiang, R., Mei, S., Zhang, S., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2960931 · Cited by 23
“NWPU VHR-10 is a challenging ten-class VHR optical remote sensing image object detection dataset [29], [30], which is used for multi-class object detection.”
BackgroundRaza, A., Huo, H., Sirajuddin, S., & Fang, T. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3021045 · Cited by 23
Taming a Diffusion Model to Revitalize Remote Sensing Image Super-Resolution
Zhu, C. N., Liu, Y., Huang, S., & Wang, F. (2025) · Remote Sensing · doi:10.3390/rs17081348 · Cited by 27
RETRACTED ARTICLE: Content based satellite image retrieval system using fuzzy clustering
Kavitha, P. K., & Saraswathi, P. V. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02064-1 · Cited by 22
RetractedLi, J., Tian, J., Gao, P., & Li, L. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323246 · Cited by 22
“Dota[1], NWPU VHR-10[2] and other data sets ship targets are only a large category, and there is no specific division of ship categories.”
BackgroundGeospatial Object Detection with Single Shot Anchor-Free Network
Guo, Y., Ji, J., Lu, X., Xie, H., & Tong, X. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323608 · Cited by 22
“Following the standard protocol [1,3], Average Precision (AP) is used as the evaluation criteria.”
MethodScene Classification in Remote Sensing Images using Dynamic Kernels
Datla, R., Chalavadi, V., & Mohan, C. K. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533648 · Cited by 22
Li, H., Hu, C., Zhong, X., Zeng, C., & Shen, H. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3224555 · Cited by 24
“Similarly, many datasets composed of remote sensing images have also been constructed to support remote sensing object detection, such as NWPU VHR-10 [39], the vehicle detection in aerial imagery dataset [40], DOTA [41], DIOR [42], and the large-scale dataset for instance segmentation”
MethodDouble-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation
Shen, L. C., Huang, X., & Fan, C. (2018) · Sensors · doi:10.3390/s18051393 · Cited by 22
“Figure 6 shows the test images (These images are taken from a very-high-resolution remote sensing image dataset constructed by Gong Cheng et al. from Northwestern Polytechnical University [70]. the rounding method.”
MethodMo, N., Li, Y., Zhu, R., & Xie, H. T. (2019) · Remote Sensing · doi:10.3390/rs11030272 · Cited by 22
“Besides, there are several other baselines to be compared with our method, including the traditional detectors BOW [45], COPD [44] and the one-stage deep learning method SSD [18], and the methods devoting to the multi-scale objects such as object detection based on multi-scale CNN [31], Remote Sensing Imagery Based on…”
MethodPolar Coordinate Convolutional Neural Network: From Rotation-Invariance to Translation-Invariance
Jiang, R., & Mei, S. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8802940 · Cited by 22
“NWPU VHR-10 is a challenging ten-class VHR optical remote sensing image data set [11], including: airplane, storage tank, tennis court, basketball court, harbor, bridge, ship, Table 3.”
BackgroundObject Detection in Aerial Images Using a Multiscale Keypoint Detection Network
Su, J., Liao, J., Gu, D., Wang, Z., & Cai, G. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3044733 · Cited by 24
“…(i.e., DOTA [14], VisDrone [15], DIOR [28] and the Northwestern Polytech-nical University ten-class geospatial object detection dataset (NWPU VHR-10) [16]) for object detection in aerial images, many studies have attempted to transfer detectors for natural images to aerial object detection.”
BackgroundLiu, L., Wang, Y., Peng, J., & Zhang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3434452 · Cited by 21
“HRSI scene classification (HRSI-SC) involves categorizing images into predefined semantic labels based on their content, significantly benefiting various applications, e.g., urban planning [1], [2], disaster monitoring [3], and geospatial object detection [4], [5].”
BackgroundRingMoGPT: A Unified Remote Sensing Foundation Model for Vision, Language, and Grounded Tasks
Wang, P., Hu, H., Tong, B., Zhang, Z., Yao, F., Feng, Y., Zhu, Z., Chang, H., Diao, W., Ye, Q., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3510833 · Cited by 35
“Specifically, for image captioning and grounded captioning tasks, we created DOTA-Cap, DIOR-Cap, DOTA-GC, DIOR-GC, and NWPU-GC datasets based on the object detection datasets DOTA [34], DIOR [35], and NWPU VHR-10 [41].”
Few-Shot Scene Classification With Multi-Attention Deepemd Network in Remote Sensing
Yuan, Z., Huang, W., Li, L., & Luo, X. (2020) · IEEE Access · doi:10.1109/access.2020.3044192 · Cited by 21
“In traditional remote sensing scene classification, traditional handcrafted features can be subdivided into three types based on the morphological differences of features: texture features, structural features, and spectral features [7].”
BackgroundRepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images
Zhou, L., Chang, Z., Yan, H., Zuo, X., Liu, Y., Qiao, B., & Yang, Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11030158 · Cited by 21
Invariant Structure Representation for Remote Sensing Object Detection Based on Graph Modeling
Zhu, Z., Sun, X., Diao, W., Chen, K., Xu, G., & Fu, K. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3181686 · Cited by 21
“In recent years, with the rapid development of CNN, it has brought considerable performance improvements to object detection task in the field of remote sensing [4], [5], [6], [7], [8], [9], [10], [11], [12].”
BackgroundKızılkaya, S., Algancı, U., & Sertel, E. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11080445 · Cited by 21
Zhou, Q., Zhang, W., Li, R., Wang, J., Zhen, S., & Niu, F. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.4.043049 · Cited by 21
Machine learning paradigms in high-resolution remote sensing image interpretation
Zhou, P., Gong, C., Yao, X., & Junwei, H. (2021) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210164 · Cited by 26
Self-citationYe, L., Wang, L., Sun, Y., Zhao, L., & Wei, Y. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1415477 · Cited by 21
“The Bag-of-Visual-Words (BoVW) modelmodel (Yang and Newsam 2010) was proposed to overcome the problems,which has been widely used to build mid-level features for aerial scenes (Cheng et al. 2014).”
MethodBeyond context: Exploring semantic similarity for small object detection in crowded scenes
Xi, Y., Zheng, J., He, X., Jia, W., Li, H., Xie, Y., Feng, M., & Li, X. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.009 · Cited by 22
“On the other hand, object detection in RSIs [13– 17] has remained as an unsolved problem, not only because of the scarcity of well annotated datasets of objects in RSIs, but also due to the huge variation in the scales of objects on the Earth’s surface, as shown in Fig.”
BackgroundMethodBroad Area Target Search System for Ship Detection via Deep Convolutional Neural Network
You, Y., Li, Z., Ran, B., Cao, J., Lv, S., & Liu, F. (2019) · Remote Sensing · doi:10.3390/rs11171965 · Cited by 21
“The size of the ship targets in the high-resolution remote sensing images varies greatly, while the size of the targets in the commonly used training dataset, such as DOTA [38], NWPU VHR-10 [39] and UCAS-AOD [40], are mostly similar and large-scale targets are rare, which results in a poor performance for large-scale…”
BackgroundTarget Detection in Remote Sensing Image Based on Object-and-Scene Context Constrained CNN
Cheng, B., Li, Z., Xu, B., Dang, C., & Deng, J. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3087597 · Cited by 21
“Two public multicategory remote sensing image datasets were applied in the experiment to verify the target detection performance of the proposed method, Northwestern Polytechnical University very high-resolution (NWPU VHR-10) [16] and high-resolution remote sensing detection (HRRSD) [17].”
MethodZeng, X., Wei, S., Wei, J., Zhou, Z., Shi, J., Zhang, X., & Fan, F. (2021) · Remote Sensing · doi:10.3390/rs13142788 · Cited by 21
Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
Körez, A., & Barışçı, N. (2019) · Applied Sciences · doi:10.3390/app10010083 · Cited by 20
“Class COPD [9] RICNN [11] RI-CAO [12] DODN [13] Ours Plane 0.”
BackgroundLee, D.-H., Kim, H., & Park, J.-H. (2021) · Agronomy · doi:10.3390/agronomy11081554 · Cited by 26
“However, when the input data are difficult to distinguish with a linear decision boundary, the kernel method can be used in SVM.”
MethodMSE-Net: A novel master–slave encoding network for remote sensing scene classification
Yue, H., Qing, L., Zhang, Z., Wang, Z., Guo, L., & Peng, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107909 · Cited by 22
Self-citationLiu, J., Jing, D., Zhang, H., & Dong, C. (2024) · Electronics · doi:10.3390/electronics13122358 · Cited by 25
“We assessed the performance of the proposed SRFAD-Net on the RSOD dataset [48] and NWPU VHR-10 [49] datasets.”
MethodAttribute profile based target detection using collaborative and sparse representation
Imani, M. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.006 · Cited by 20
“A rotation-invariant method for object detection has been introduced in [25] that utilizes a collection of part detectors.”
MethodAF-SSD: An Accurate and Fast Single Shot Detector for High Spatial Remote Sensing Imagery
Yin, R., Zhao, W., Fan, X., & Yin, Y. (2020) · Sensors · doi:10.3390/s20226530 · Cited by 21
“Our AF-SSD, with mAP 88.7%, average running time 0.035 s per image, and parameters 5.7 M, outperforms most of state-of-the-art methods in the NWPU VHR-10 dataset.”
BackgroundMethodInfluentialLi, Y., He, B., Melgani, F., & Long, T. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3076072 · Cited by 20
OLCN: An Optimized Low Coupling Network for Small Objects Detection
Yuan, Y., & Zhang, Y. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3122190 · Cited by 20
Local to Global: A Sparse Transformer-Based Small Object Detector for Remote Sensing Images
Li, Z., Wang, Y., Feng, H., Chen, C., Dongdong, X., Zhao, T., Gao, Y., & Zhao, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3525720 · Cited by 22
“Extensive experiments are conducted on three large-scale remote sensing datasets: DIOR [18], AI-TOD [19], and NWPU VHR-10 [20].”
AOPDet: Automatic Organized Points Detector for Precisely Localizing Objects in Aerial Imagery
Zhu, Z., Sun, X., Diao, W., Chen, K., Xu, G., & Fu, K. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3093557 · Cited by 20
Cai, Y., & Li, K. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1368205 · Cited by 19
Wang, Z., Lu, D., Zhang, D., Sun, M., & Zhou, Y. (2016) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-016-0429-9 · Cited by 19
A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification
Shen, J., Zhang, T., Wang, Y., Wang, R., Wang, Q., & Qi, M. (2021) · Remote Sensing · doi:10.3390/rs13030433 · Cited by 19
“Instead, the local features such as SIFT [33] and HOG [1,17,18], describe the image in each local region, and mid-level descriptors are needed to compute the statistics of these local features.”
MethodCao, L., Zhang, X., Wang, Z., & Ding, G. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1910371 · Cited by 19
“NWPU VHR-10 (northwestern polytechnical university, very-high-resolution) dataset (Cheng et al. 2014; Cheng et al. 2016a; Cheng, Zhou, and Han 2016b) is a publicly available 10-class geospatial object detection dataset used for research purposes only.”
BackgroundTranslation of Aerial Image Into Digital Map via Discriminative Segmentation and Creative Generation
Fu, Y., Liang, S., Chen, D., & Chen, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3110894 · Cited by 18
“In addition, because of the high labeling cost of pixelwise labels, more aerial image datasets are annotated with objectwise labels, e.g., DOTA [51], VHR-10 [52]–[54], HRRSD [55], and DIOR [56].”
BackgroundZhang, T., Zhuang, Y., Chen, H., Chen, L., Wang, G., Gao, P., & Dong, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3277588 · Cited by 19
“…detection performance gain by bridging the domain gap between natural scene domain and RSD. Finally, when employing our proposed OCMIM-based SSP strategy, as shown in the ninth row of Tables II and III, it obtains the highest mAP, i.e., 69.5% mAP and 89.3% mAP on DIOR [32] and NWPUVHR10 [31].”
BackgroundMethodInfluentialLearning Remote Sensing Object Detection With Single Point Supervision
He, S., Zou, H., Wang, Y., Li, B., Cao, X., & Jing, N. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3343806 · Cited by 19
“Besides, a number of remote-sensing (RS) datasets (e.g., HRSC2016 [8], NWPU VHR-10 [9], and DOTA series [10]) containing accurate and rich annotations were proposed to develop and benchmark RSOD methods.”
BackgroundFew-Shot Object Detection With Multilevel Information Interaction for Optical Remote Sensing Images
Wang, L., Mei, S., Wang, Y., Lian, J., Han, Z., & Chen, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3410308 · Cited by 19
“NWPU VHR-10 [60] contains 800 high-resolution satellite images, which are cropped from the Google Earth and Vaihingen datasets, and then manually annotated by experts.”
Liu, N., Lu, X., Wan, L., Huo, H., & Fang, T. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7030095 · Cited by 18
“[5] proposed to train part detectors by using HOG feature pyramids to”
BackgroundMethodInfluentialXu, G., Zhu, X., & TAPPER, N. J. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1714774 · Cited by 19
“…for land cover mapping (Tokarczyk et al. 2015; Zhang, Du, and Zhang 2015; Cheng et al. 2015; Zhang et al. 2014; Han et al. 2014; Cheriyadat 2014; Cheng et al. 2014; Yang and Newsam 2013), urban planning (Hu et al. 2015b), and target detection (Tao et al. 2011; Aytekin, Zöngür, and Halici 2013;…”
BackgroundJiang, R., Mei, S., Ma, M., & Zhang, S. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3021283 · Cited by 17
“NWPU VHR-10 is a challenging ten-class VHR optical remote sensing image data set [7], [39].”
BackgroundZhu, D., Xia, S., Zhao, J., Zhou, Y. Z., Niu, Q., Yao, R., & Chen, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02335-0 · Cited by 18
Yan, P., Qi, X., & Jiang, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321026 · Cited by 17
“Ablation and comparative experiments were conducted on the RSOD [27] and NWPU VHR-10 [28–30] datasets, while generalization experiments were performed on the TinyPerson, LEVIR-ship, brain-tumor, and smoke_fire_1 datasets.”
Xu, Z., Gao, R., Huang, K., & Xu, Q. (2022) · Remote Sensing · doi:10.3390/rs14184676 · Cited by 17
HFPNet: Super Feature Aggregation Pyramid Network for Maritime Remote Sensing Small-Object Detection
Chen, C., Zeng, W., & Zhang, X. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3286483 · Cited by 17
“4) We constructed a dataset for remote sensing targets (MTDS) and validated our model on the environmentenhanced MTDS and NWPU-VHR-10 datasets, achieving the best detection performance on each.”
BackgroundInfluentialDong, Z. H., Lin, B., & Xie, F. (2024) · Remote Sensing · doi:10.3390/rs16030525 · Cited by 17
Wu, J., Qin, C., & Feng, G. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3385399 · Cited by 17
“…of our proposed SMDC-Net fairly and intuitively, we conduct experiments with other methods on two widely recognized datasets: DIOR [16] and NWPU VHR-10 [17] and split them into base and novel categories, which is consistent with the settings of the corresponding dataset for the other methods [5].”
MethodJobaer, S., Tang, X., Zhang, Y., Li, G., & Ahmed, F. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01676-w · Cited by 17
Yu, Y., Ai, H., He, X., Yu, S., Zhong, X., & Lu, M. (2018) · IEEE Access · doi:10.1109/access.2018.2881479 · Cited by 17
“In addition, to improve the versatility of the model, we use the public data set NWPU VHR-10 [33]–[35] for testing.”
MethodInfluentialEnhancing Mid–Low-Resolution Ship Detection With High-Resolution Feature Distillation
He, S., Zou, H., Wang, Y., Li, R., Cheng, F., Cao, X., & Li, M. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3110404 · Cited by 17
Zhao, B., Wang, Q., Wu, Y., Cao, Q., & Ran, Q. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3188252 · Cited by 17
DualDA-Net: Dual-Head Rectification for Cross-Domain Object Detection of Remote Sensing
Zhu, Y., Sun, X., Diao, W., Wei, H., & Fu, K. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3285747 · Cited by 16
“Specifically, the features are generated by the model trained on the NWPU VHR-10 [12] dataset. the location and categorizing objects in images.”
BackgroundMethodInfluentialDiscriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images
Guo, M., You, Y., & Liu, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3326992 · Cited by 18
“NWPU VHR-10 [50] is a very-high-resolution RSI dataset collected from Google Earth and the ISPRS Vaihingen dataset.”
BackgroundLi, Z., Wang, Y., Zhang, Y., Gao, Y., Zhao, Z., Feng, H., & Zhao, T. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3356507 · Cited by 16
“2) NWPU VHR-10 [5] consists of 800 images with 3651 instances serving for optical remote image object detection task.”
BackgroundSong, S., Liu, J., Pu, H., Liu, Y., & Luo, J. (2019) · Remote Sensing · doi:10.3390/rs11121435 · Cited by 16
Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors
Zheng, X., Zhang, Y., Hong, S., Li, H., Tang, L., Xiong, Y., Zhou, J., Wang, Y., Sun, X., Zhu, P., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3069250 · Cited by 16
Deshpande, A. M., Patale, S. R., & Roy, S. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1957512 · Cited by 17
“The image dataset used for training and testing of the deep convolutional network model are as follows: (1) The dataset used is NWPU (Northwestern Polytechnical University) VHR-10 (Very High Resolution-10) (CHENG et al. 2014).”
MethodRemote Sensing Image Scene Classification Based on Global Self-Attention Module
Li, Q., Yan, D., & Wu, W. (2021) · Remote Sensing · doi:10.3390/rs13224542 · Cited by 16
Mutual-Feed Learning for Super-Resolution and Object Detection in Degraded Aerial Imagery
Yang, J., Fu, K., Wu, Y., Diao, W., Dai, W., & Sun, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3198083 · Cited by 16
“To ensure the greatest possible diversity and complexity of the object categories, extensive experiments are performed over NWPU VHR-10 [10], COWC [11], and FAIR1M [12] datasets to evaluate the proposed structure.”
MethodInfluentialChaib, S., Mansouri, D. E. K., Omara, I., Hagag, A., Dhelim, S., & Bensaber, D. A. (2022) · Remote Sensing · doi:10.3390/rs14225817 · Cited by 16
VehSat: a Large-Scale Dataset for Vehicle Detection in Satellite Images
Drouyer, S. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323289 · Cited by 16
Large Kernel Separable Mixed ConvNet for Remote Sensing Scene Classification
Zhang, K., Cui, T., Wu, W., Zheng, X., & Cheng, G. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3353796 · Cited by 15
“Cheng et al. [20] devised a pragmatic rotation-invariant framework grounded in an ensemble of partial detectors.”
BackgroundSelf-citationXing, Y., Han, G., Mao, H., He, H., Bo, Z., Gong, R., Ma, X., & Gong, W. Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3545034 · Cited by 20
Open-Vocabulary Object Detection in UAV Imagery: A Review and Future Perspectives
Zhou, Y., Li, J., Ou, C., Yan, D., Zhang, H., & Xue, X. (2025) · Drones · doi:10.3390/drones9080557 · Cited by 22
Shen, J., Zhang, C., Zheng, Y., & Wang, R. (2021) · Remote Sensing · doi:10.3390/rs13183579 · Cited by 15
“As one of the basic tasks of understanding and recognizing remote sensing images, optical remote sensing image scene classification targets at automatically labelling the scene category (such as airport, forest, residential area, church) according to the semantic content of each image [1], which plays an important rol…”
BackgroundChen, Y., Zhang, Z., Chen, Z., Zhang, Y., & Wang, J. (2022) · Remote Sensing · doi:10.3390/rs14184566 · Cited by 16
“HRSC2016 dataset [28], NWPU-RESISC45 dataset [29], and NWPU VHR-10 dataset [30].”
MethodAiTLAS: Artificial Intelligence Toolbox for Earth Observation
Dimitrovski, I., Kitanovski, I., Panov, P., Kostovska, A., Simidjievski, N., & Kocev, D. (2023) · Remote Sensing · doi:10.3390/rs15092343 · Cited by 18
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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