The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Multi-class geospatial object detection and geographic image classification based on collection of part detectors
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“or to train independent detectors separately for each orientation [167, 168].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [168]
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Bag-of-visual-words and spatial extensions for land-use classification
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Research on Attention-Based Multigranular Remote Sensing Image Extreme Object Detection Algorithm
Li, M., Tsang, E. C. C., Xu, W., & He, Q. (2024) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc63072.2024.10935211
Context-awareness and frequency-refinement network for small object detection in aerial images
Liu, K., Zuo, X., Ma, X., Wang, C., & Yang, S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04152-1
Chintalapati, B., Jafari, A., Laufer, R., Liwicki, M., & Eickhoff, J. (2026) · CEAS Space Journal · doi:10.21203/rs.3.rs-6555469/v1
RemoteDPL: A Semi-Supervised Object Detector With Dense Pseudo-Labels for Remote Sensing
Ma, Y., Zhou, X., Lan, S., Wang, W., Sun, Z., & Qiao, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3581206
“0 [43] and NWPU VHR-10 [44] datasets, respectively.”
Improving Remote Sensing Object Detection Based on Relation DETR
Yan, Z., & Hsiang, H.-C. (2025) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda64898.2025.11030416
“In the context of the NWPU VHR-10 dataset[7], we conducted a comparative analysis of the enhanced RelationDETR against other object detection methodologies[2].”
VHRV: Very High-Resolution Benchmark Dataset for Vessel Detection
Büyükkanber, F., Yanalak, M., & Musaoğlu, N. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101641
Suzdalev, I., Maršalka, B., & Daugėla, I. (2025) · Preprints.org · doi:10.20944/preprints202508.1162.v1
Open-Vocabulary Object Detection for Remote Sensing ImagesPreprint
Li, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5434798
Research on Remote Sensing Small Object Detection Based on Improved YOLOv8
Weng, S., & Jing, M. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00213
“To comprehensively and rigorously evaluate the performance of the SSE-YOLO model in small object detection tasks from drone-based remote sensing, this study utilizes the NWPU VHR-10 dataset [16] for experimental validation.”
DCGAN Feature-Enhancement-Based YOLOv8n Model in Small-Sample Target Detection
Zheng, P., Cheng, Y., Zhu, W., Liu, B., Ye, C., Wang, S., Liu, S., & Bai, J. (2025) · Computers · doi:10.3390/computers14090389
“The dataset is constructed using a two-stage quality control mechanism [16,17]: First, the initial screening is performed by 0.5–2 m resolution Google Earth images, and then, 85 ultra-high-definition CIR images measuring 0.08 metres were introduced to enhance detail features.”
InfluentialELFT: Efficient local-global fusion transformer for small object detection
Hua, G., Wu, F., Hao, G., Xia, C., & Li, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332714
“NWPU VHR-10 dataset [34–36] is a public remote sensing dataset released by Northwest-ern Polytechnic University.”
A Fast Rotation Detection Network with Parallel Interleaved Convolutional Kernels
Deng, L., Sun, L., & Li, H. (2025) · Symmetry · doi:10.3390/sym17101621
Wu, X., & Ablameyko, S. (2025) · «System analysis and applied information science» · doi:10.21122/2309-4923-2025-3-4-10
Adaptive Feature Recalibration Network for More Accurate Instance Segmentation in Remote Sensing
Yang, X. Q., & Mohamed, A. S. A. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3628858
“To comprehensively evaluate the performance of our proposed method, we conduct experiments on six widely used benchmark datasets: HRSC2016 [48], SCCOS [49], DOTA [50], and DIOR-R [51] for rotated object detection, and VHR10-COCO [52] and iSAID [53] for instance segmentation.”
FASE: Feature-Aligned Scene Encoding for Open-Vocabulary Object Detection in Remote Sensing
Hwang, H., & Woo, S. S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760838
“The dataset comprises two components: LAE-FOD consists of labeled data from DOTA [6], DIOR [12], FAIR1M [21], NWPU VHR-10 [3], RSOD [18], Xview [9], HRSC2016 [17], and Condensing-Tower [4].”
Abdullahi, S., Gital, A. Y., Zambuk, F. U., Mustapaha, S. W., & Abdullahi, A. U. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1410000059
Dan, Z., Xu, C., Wan, S., Li, Q., Xiao, G., & Lv, C. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227364
“To effectively advance deep learning-based methods for remote sensing image object detection, many organizations and researchers have publicly released various large-scale datasets, such as NWPU VHR-10 [1], HRRSD [2], DOTA [3] and DIOR [4].”
LCB-Net: Long-Range Context and Box Distribution Network for Small Object Detection
Qiao, Y., Liang, Y., & Liu, M. (2025) · Electronics · doi:10.3390/electronics14224487
“We evaluate our method on three specialized SOD datasets, VisDrone [42], NWPU-VHR-10 [43], and WidePerson [44], which collectively cover pedestrian detection in complex scenes, multi-scale remote sensing object recognition, and high-density aerial object detection.”
Prototype Metric Decision for Few-shot Object Detection in Optical Remote Sensing Imagery
Zhang, T., Zhuang, Y., Wang, G., Zhang, W., Zhu, Y., Chen, H., & Li, L. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242818
“To illustrate the effectiveness of the proposed Proto-RCNN, a public optical remote sensing dataset NV10 [7] is introduced, which totally includes 650 available images.”
A U-Net Based Image Stitching Detection and Segmentation for Image Stitching Assessment
Tavasoli, A. F., & Razzazi, F. (2025) · doi:10.1109/ipria68579.2025.11263551
“Another dataset from which images were selected is the NWPU VHR-1012 dataset [12].”
Wang, Z., Xiao, Y., Fu, Z., Li, M., & Li, N. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3639298
“In the RSISR task the features on the original feature map can enhance the final channel weights to a certain extent, so this article introduces the residual connection in the channel enhancement attention module [55].”
LFODet:Lightweight Few-Shot Object Detection with Meta-Learningin Remote Sensing ImagesPreprint
Fang, X., Hou, Y. F., Yang, X., Wu, H., & Xiong, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5936819
Aghili, M. E., Ghassemian, H., & Imani, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5477867
Zhou, L., Duan, W., Luo, Z., Pan, Y., Wang, C., Wang, S., Zhao, W., & Lao, J. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.12.071
A unified deep learning framework for segmentation in remote sensing imagery
Lokula, B., Kishore, P. V. V., & Prasad, L. V. N. (2025) · Discover Computing · doi:10.1007/s10791-025-09774-0
“However, when applied to remote sensing images (RSI), most of these techniques show performance degradation [2–6].”
InfluentialFReID: advancing small-target object detection with feature reintegration and distribution
Miao, Z., Zhang, Q., He, M., Li, Y., Bi, X., Wang, J., & Hou, Q. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-025-08143-4
Zhou, Y., Wang, J., & Li, X. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342716
Multiwavelet Based Multibranch Skip Feature Fusion Network for Remote Sensing Image Super Resolution
Sandana, J. S., Deivalakshmi, S., & Asari, V. K. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3654465
“The three standard remote sensing image datasets used for testing are the AID test Dataset [59], the NWPU VHR-10 dataset [60], and the UC Merced dataset [61].”
TRIMED-DPDC: Triple-Median Filtering-Based Defective Pixel Detection and Correction Algorithm
Peng, L., Huang, Y., Wang, M., Li, W., & Zeng, X. (2025) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipw68931.2025.11386413
“The experimental evaluation is conducted using images from the NWPU VHR-10 dataset [28].”
A Survey of Object-Detection Datasets Applied to Defense Environments
Jeong, Y., & Noh, S. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11388154
“This study examines six publicly available aerial optical datasets: iSAID [3], FAIR1M [4], VEDAI [5], NWPU VHR-10 [6], SODA-A [7], MAR20 [8], and RSOD [9].”
Robust and Arbitrary Rotation Invariant Local Descriptor Learning
Bao, W., Geng, Z., Wei, X., & Liu, H. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3668689
“NWPU VHR-10 [55] is a remote sensing image dataset comprising 10 object classes.”
Dual Imbalance Suppression for Source-Free Object Detection in Aerial Images
Ma, Y., Chai, L., Mao, S., & Zhang, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3670884
“NWPU VHR-10 [45] is composed of 800 remote sensing images, of which 715 images are derived from Google Earth, and 85 images are acquired from the Vaihingen dataset.”
GeoSolver: Scaling Test-Time Reasoning in Remote Sensing with Fine-Grained Process Supervision
Sun, L., Fu, R., Duan, Z., Liu, H., Liu, X., & Yang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37035-8_4 · Cited by 3
Peng, B., Chen, Y., Ma, C., Jiang, B., Liao, N., & Zhu, M. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3673202
“5 [20] and NWPU VHR-10 [21].”
S-MDMA: Sensitivity-Aware Model Division Multiple Access for Satellite–Ground Semantic Communication
Hui, C., Meng, R., Han, S., Gao, S., Xu, X., & Zhang, P. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3679535
“To assess the generalizability of the proposed model, we adopt the NWPU VHR-10 dataset [38] for further validation.”
Zhu, Y., Huang, B., Zhou, F., & Liu, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115915
Info‐ YOLO : A Novel Multiscale Feature Enhancement Architecture for Remote Sensing Object Detection
Wang, Y., Gao, Y., & Zhao, Y. (2026) · Expert Systems · doi:10.1111/exsy.70255
“The NWPU VHR-10 dataset (Cheng et al. 2014) comprises 800 satellite images sourced from Google Earth, including 650 positive samples (with objects) and 150 negative samples (without objects), featuring ground resolutions between 0.5 m and 2 m.”
InfluentialMSDNet:A Multi-scale and Multi-domain Remote Sensing Satellite Image Object Detection methodPreprint
Li, Y. Q., Wang, Z., Liu, J., Chen, Y., & Yin, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6543204
ENHF-YOLO: Enhanced High-Frequency Domain Feature Extraction of Small Targets in Remote Sensing
Chang, B., Shi, H., Jin, H., Liu, G., Han, L., Wei, H., & Yuan, P. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464283
Qiu, Y. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2663566
“NWPU VHR-10 is a high-resolution geospatial object detection dataset (Cheng et al. 2014).”
VISO: Accelerating In-Orbit Object Detection with Language-Guided Mask Learning and Sparse Inference
Wang, M., & Qiu, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02163
A Lightweight Target Detection Algorithm for Remote Sensing Images
Zhou, L., & Chen, G. (2026) · International Conference on Big Data, Communication Technology and Computer Applications (BDCTA) · doi:10.1145/3801359.3801425
Le, X., Chen, Y., Zeng, W., Ao, X., Chen, H., & Xie, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114976
Shallow-water reef detection driven by few-shot learning with airborne LiDAR bathymetry
Su, D., Bi, G., Yang, A., Xiong, X., Wang, C., Wang, Z., Fanlin, Y., & Gao, H. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.05.021
Object detection in remote sensing
Hänsch, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-343796-0.00023-1
An, X., Zhong, C., Sun, J., Liu, J., & He, W. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.05.023
Li, C. Y., Zhang, Q., Liang, X., Xiao, Y., Li, S., & Wang, P. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3696309
“NWPU VHR-10 [48] contains 800 target object samples and 100 high spatial resolution samples only containing backgrounds, with ground sampling distances ranging from 0.5 to 2 m.”
InfluentialMLS-YOLO : Remote sensing target detection for multi-scale craters
Zhu, L., Lu, B., Yao, H., Ren, S., Chen, Z., & Liu, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132952
Chen, H., Li, J., Liu, Y., Zhuang, Y., Xu, M. M., Dong, H., Xie, Y., & Chen, L. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3697113
“In addition, we evaluate our method on the object detection dataset NWPU VHR-10 [42].”
Li, Y., Bai, Y., Ma, J., Temirbayev, A. A., Li, Y., Shang, C., & Shen, Q. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3699883
“NWPU VHR-10 [55]: NWPU VHR-10 is a commonly used small-scale remote sensing object detection dataset, containing 800 images, of which 650 are annotated, with a total of 3896 instances.”
Yu, J., Li, H., Zheng, X., Zhong, C., & Sun, Q. (2026) · Remote Sensing · doi:10.3390/rs18121910
NH-CD 2 : Neck-Head Combined Double-Branch Distillation for Generalized Few-Shot Object Detection
Zhang, J., Sisi, H., Hong, Z., Yu, X., & Li, Y. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3704624
“3) Experimental results on the NWPUv2 [15] dataset validate the e ff ectiveness of the proposed approach.”
陶晓焦, Feng, Q., Li, Y., Hao, W., & Zhang, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00828-x
“In addition, to further evaluate the performance of the proposed method in remote sensing scenarios, we conducted experiments on NWPU VHR-10 (Cheng et al. 2014) and AID datasets (Xia et al. 2017).”
InfluentialZhang, M., Ma, K., Wu, H. W., Liu, L., Feng, J., & Jiao, L. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.115821
Multiscale feature optimization for accurate small object detection in remote sensing imagery
Wang, B., Zhou, M., Ma, W., Li, T., & Zhu, C. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01866-1
AMFNet: adaptive multi-scale fusion network for small object detection
Tian, B., Hu, M., Li, L., Zhao, X., Tang, M., & Wang, Z. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02369-2
XS-YOLO: A Lightweight NMS-Free Framework for Small Object Detection in Remote Sensing Imagery
Wang, Z., Guo, C., Zhang, Z., Guo, M., & Li, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3504-9_47
Bao, J., Zhang, Z., Petropoulos, G. P., Fang, Z., Shen, J., & Li, J. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.102168
Cao, B., Chen, H., & Zhou, P. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3538-4_20
Self-citationFrom Recognition to Reasoning: Edge-Frequency Chain-of-Thought for Remote Sensing Object Detection
Li, X., Zhao, Y., Yuan, B., Zhao, Y., & Yao, R. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3715352
“Table I reports the category definitions, sample sizes, training / validation / test splits, and annotation information of these datasets [45], [46], [47].”
Shi, X., & Zhu, J. (2026) · International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) · doi:10.1109/icbase70763.2026.11619493
“NWPU VHR-10 is a common optical remote sensing benchmark with ten object categories [1], [2].”
DynaFocus-DETR: Dynamic Feature-Focused Detection Transformer for Remote Sensing Detection
Zhang, H., Lyu, W., Guo, Q., Xu, Y., & Deng, Z. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3718484
“2) NWPU VHR-10 [18]: It consists of 650 high-resolution remote sensing images.”
Li, Y., Bai, Y., Wu, Z., Zhang, X., Li, Y., Shang, C., & Shen, Q. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116774
Huang, S., Tian, L., & Zhang, Y. (2026) · Electronics · doi:10.3390/electronics15153449 · Cited by 1
Chen, J., & Chen, H. (2026) · IET Image Processing · doi:10.1049/ipr2.70443
Maturity-Gated Anti-Forgetting Sampling for Remote-Sensing Object Detection Training
Zhou, Y., Ning, C., Zhong, L., & Zhang, X. (2026) · Remote Sensing · doi:10.3390/rs18162657
Foundations of Remote Sensing and Earth Observation
Gibbes, C., Jane Southworth, B., Bunting, E., Rahaman, M., Safaei, M., Chakraborty, T., Hasan, M. M., Chaudhary, A., & Smith, A. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26741-3.00015-5
Zhao, X., Zhang, J., Li, L., Shao, X., Yu, N., & ren, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae9788
“To evaluate the performance of I 2 P-Det in small object detection, three representative datasets-VisDrone2019 [42], NWPU VHR-10 [43], and RSOD [44]-are selected in this experiment.”
LS-YOLO: A Lightweight Remote Sensing Object Detection Algorithm Based on YOLOv8
Wei, Z., Bo, S., Lu, Y., Hao, X., & Wang, Y. (2026) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd69640.2026.11637205
“To validate the effectiveness and generalization of the proposed method, two public high-resolution remote sensing datasets were adopted, namely RSOD [12] and NWPU VHR-10 [13].”
CFPS-RTDETR: a feature-enhanced RT-DETR for small object detection in drone-captured aerial scenes
Cao, Y., Cui, M., Feng, H., & Wang, C. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08804-y
Text Semantic Prior Contrastive Learning for Few-Shot Remote Sensing Object Detection
Liu, W., Pei, Y., Wang, J., Wei, T., Gu, P., & Chen, L. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3726093
“…effectiveness of the proposed TSP-Net method, extensive experiments conducted on multiple few-shot remote sensing benchmarks, including DIOR [5] and NWPU VHR-10 [15] with different dataset splits, demonstrate that the proposed method consistently and significantly outperforms existing approaches.”
Zhang, P., Zhang, J., Liu, J., Li, X., Liu, Y., & Tan, L. (2026) · Remote Sensing · doi:10.3390/rs18172872
“…detection tasks, two publicly available and widely adopted benchmark datasets were selected for this study, namely, the object detection in optical remote sensing images (DIOR) dataset [77] and the Northwestern polytechnical university very high resolution 10-class (NWPU VHR-10) dataset [78].”
Ma, W., Hu, Z., Zhu, H., Lv, X., Hou, W., & Ma, M. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3726422
“This article utilizes three datasets to evaluate the proposed method: NWPU VHR-10 [51], VEDAI [52], and DIOR [53].”
Zhou, M., Zhou, Y., & Song, K. (2026) · PLoS ONE · doi:10.1371/journal.pone.0353309
“We comprehensively evaluated the proposed DAAF framework through large-scale experiments on three widely adopted aerial remote sensing detection benchmarks: NWPU VHR-10 [51], DOTA v1.”
Cascaded Multi-Scale Aggregation With Hierarchical Task Alignment for Aerial Small Object Detection
Cao, B., Yang, Z., Kan, M., Yang, Y., Wang, W., Chen, W., Wang, Y., & Zhou, P. (2026) · IEEE Access · doi:10.1109/access.2026.3727614 · Cited by 1
“Experiments are conducted on three aerial object-detection benchmarks: VisDrone-2019 [2], RSOD [33], and NWPU VHR-10 [34].”
Self-citationZhang, J., & Zhang, F. (2026) · Applied Sciences · doi:10.3390/app16178616
Collaborative Context-Affine Perception Network for Remote Sensing Small Object Detection
He, W. P., Xu, Y., Qi, Q., Zhang, G., & Tu, B. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3729695
“3) NWPU VHR-10 [32]: Released by Northwestern Poly-technic University in 2014, the NWPU VHR-10 dataset is a commonly used resource in remote sensing applications.”
Prototype-Conditioned Generative Adversarial Network for Few-Shot Remote Sensing Object Detection
Jiang, Z., Yang, G., Ma, D., & Yuan, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3730249
“V2: NWPU VHR-10.v2 [47] is a benchmark dataset curated by Northwestern Polytechnical University for object detection in very high-resolution remote sensing imagery.”
C2Mamba: Collaborative context state space model for remote sensing object detection
Lin, Z., He, Z., Ling, X., Liang, H., An, J., Su, W., Tan, J., Guo, J., & Shi, M. (2026) · Science China Technological Sciences · doi:10.1007/s11431-025-3285-3
“[55], including air-plane (AL), ship (SP), storage tank (ST), baseball diamond (BD), tennis court (TC), basketball court (BC), ground track field (GF), harbor (HB), bridge (BG), and vehicle (VH).”
Wang, Z., Hu, X., Zhang, Z., Zhao, Q., & Zhang, Y. (2026) · Measurement · doi:10.1016/j.measurement.2026.123062
Cao, Z., Zhang, M., Genga, K., Hou, B., Ge, Y., Wang, Y., & Jiao, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135076
Qin, Z., Xie, Q., Li, Z., & Wang, C. (2026) · Structures · doi:10.1016/j.istruc.2026.113055
Wang, Y., & Yuan, H. (2026) · Springer proceedings in physics · doi:10.1007/978-981-92-4349-5_39
Chen, S., Zhang, Y., & Zhao, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135146
Exploratory Study of YOLO26 for High-Resolution Aircraft Detection in Indian Satellite Images
Waghmare, P., Sarkar, M., Rewatkar, R. M., & Pacharaney, U. (2026) · International Conference on Modern Sustainable Systems (CMSS) · doi:10.1109/cmss69636.2026.11688204
“Large aerial benchmarks such as DOTA [19] and NWPU VHR-10 [20] support this line of work, and aircraft specifically are the focus of dedicated datasets such as RarePlanes [21].”
Pan, W., & Lv, X. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3734955
“To evaluate generalization across di ff erent data distributions, the NWPU VHR-10 dataset [14] is also used.”
Wu, H., Zhu, J., Feng, G., Sun, Y., & Chen, C. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2734356
Koletsis, P., Angelidis, G., Efthymiou, V., Michail, D., Chalkias, C. N., Lagkas, T., Sarigiannidis, P., & Papadopoulos, G. T. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3737028
Adversarial Patch and Camouflage Attacks on Aerial Object Detection: A Geometry-Aware Review
Shrestha, S., Mahima, K. T. Y., & Perera, A. G. (2026) · Sensors · doi:10.3390/s26196057
MERT-DETR: Multiorder Gated and Edge-Enhanced Transformer for Remote Sensing Small-Object Detection
Shi, C., Zhu, Y., Zhang, C., & Zhu, C. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3734290
“To evaluate the model in complex remote sensing scenarios, we utilized three datasets, namely RSOD [19], NWPU VHR-10 [20], and VisDrone2019 [21].”
MSAANet: multi scale adaptive attention network in remote sensing images
Cheng, 苏. S., Chen, Y., Li, K., & Luo, X. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01764-2
Baisura, S., Srinivasan, T. K., Anju Anil, J., Revathy, J., Rajesh, V., & Kumaran, V. N. S. (2026) · International Conference on Innovation & Growth in Next-Gen Intelligent Technology & Engineering (IGNITE) · doi:10.1109/ignite69592.2026.11706221
Revisiting fragile watermarking recovery: a deep learning-based image restoration framework
Saritas, O. F. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05734-3
HMA-YOLO: Hypergraph-guided multi-scale feature interaction for remote sensing object detection
Zhang, Z., Qian, Y., & Bie, Q. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106549
A lightweight multi-scale remote sensing image detection algorithm with accelerated convergence
Guan, L., Gong, J., Zhang, L., & Ruixiang, W. U. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135288
A Lightweight Model for Small Object Detection in UAV Aerial Imagery
Yao, Y., & Wang, M. (2026) · International Conference on Navigation, Detection and Control (CNDC) · doi:10.1109/cndc70899.2026.11708430
Image recovery from severe cropping using rook polynomials and Pix2PixGAN
Saritas, O. F. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104662
CRS-Det: Consistent Region Semantic Modeling for Remote Sensing Open-Vocabulary Object Detection
Liu, M., Li, J., Ren, D., & Sun, H. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3741382
Trajectory-SAM: Object Trajectory-Guided Segmentation for Remote Sensing Videos
Guo, S., Ozel, C., & Qu, Y. (2026) · Remote Sensing · doi:10.3390/rs18203469
Song, G., Chen, K., Chu, G., 宋鸣, Zhao, C., & Sun, Q. (2026) · Electronics · doi:10.3390/electronics15204728
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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