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

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

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

DOI 10.1016/j.isprsjprs.2014.10.002

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The pascal visual object classes (VOC) challenge
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Object Detection with Discriminatively Trained Part-Based Models
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Bag-of-visual-words and spatial extensions for land-use classification
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Poselets: Body part detectors trained using 3D human pose annotations
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Automatic Aug-Aware Contrastive Proposal Encoding for Few-Shot Object Detection of Remote Sensing Images
Ma, S., Hou, B., Wu, Z., Li, Z., Guo, X., Ren, B., & Jiao, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3294943 · Cited by 17
“NWPU VHR 10 [39] is a small-scale, ten-category geospatial object detection dataset.”
Background
TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion
Qiang, W., Feng, W., Yao, L., Zhuang, C., Liu, B., & Chen, L. (2023) · Remote Sensing · doi:10.3390/rs15153883 · Cited by 15
“Furthermore, we extended our experiments to the publicly available remote sensing dataset NWPU VHR-10 [35].”
Parameter Efficient Fine-Tuning via Cross Block Orchestration for Segment Anything Model
Peng, Z., Xu, Z., Zeng, Z., Xie, L., Tian, Q., & Shen, W. (2024) · 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/cvpr52733.2024.00359 · Cited by 19
Few-Shot Object Detection for Remote Sensing Imagery Using Segmentation Assistance and Triplet Head
Zhang, J., Hong, Z., Xu, C., & Li, Y. (2024) · Remote Sensing · doi:10.3390/rs16193630 · Cited by 15
Scene classification of remote sensing image based on deep network grading transferring
Yang, Z., Mu, X.-D., & Zhao, F. (2018) · Optik · doi:10.1016/j.ijleo.2018.04.092 · Cited by 15
Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method
Liu, C., Cheng, I., & Basu, A. (2018) · Remote Sensing · doi:10.3390/rs10101544 · Cited by 15
Cascaded Detection Framework Based on a Novel Backbone Network and Feature Fusion
Tian, Z., Wang, W., Zhan, R., He, Z., Zhang, J., & Zhuang, Z. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2924086 · Cited by 15
“Polytechnical University (NWPU) VHR 10 dataset [26], is employed.”
Method
Global Context Parallel Attention for Anchor-Free Instance Segmentation in Remote Sensing Images
Liu, X., & Di, X. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3023124 · Cited by 15
A Fast and Accurate Few-Shot Detector for Objects with Fewer Pixels in Drone Image
Gao, Y., Hou, R., Gao, Q., & Hou, Y. (2021) · Electronics · doi:10.3390/electronics10070783 · Cited by 15
“In NWPU VHR-10, there are only 650 images with 10 classes of labeled objects.”
MethodInfluential
A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification
Shi, C., Zhang, X., Wang, T., & Wang, L. (2022) · Remote Sensing · doi:10.3390/rs14133184 · Cited by 15
Research on Remote Sensing Image Target Recognition Based on Deep Convolution Neural Network
Han, X., Jiang, T., Zhao, Z., & Lei, Z. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420540154 · Cited by 18
Convolution Neural Network Architecture Learning for Remote Sensing Scene ClassificationPreprint
Chen, J., Huang, H., Peng, J., Zhu, J., Chen, L., Li, W., Sun, B., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09614 · Cited by 17
“Scale-invariant feature transform (SIFT) [27], [28] and histogram of oriented gradients [4], [29] are local features that usually need mid-level descriptor to generate the entire representation [27], [6].”
Background
An online continual object detector on VHR remote sensing images with class imbalance
Chen, X., Jiang, J., Li, Z., Qi, H., Li, Q., Liu, J., Zheng, L., Liu, M., & Deng, Y. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105549 · Cited by 14
Dataset and Benchmark for Ship Detection in Complex Optical Remote Sensing Image
Hu, J., Zhi, X., Shi, T., Wang, J., Li, Y., & Sun, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3465504 · Cited by 14
“In 2014, Northwestern Polytechnical University (NWPU) released the NWPU VHR-10 dataset [4], which consists of images extracted from Google Earth and Vaihingen sources.”
Method
Automatic and Fast PCM Generation for Occluded Object Detection in High-Resolution Remote Sensing Images
Qiu, S., Wen, G., Deng, Z., Fan, Y., & Hui, B. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2731863 · Cited by 13
“W ITH the rapid development of remote sensing technologies, the remote sensing community has witnessed an alternation of detection subjects from large objects, such as airports [2], or residential areas [3], to smaller ones, such as airplanes [1], [4], [5], ships [6], and cars [7].”
BackgroundMethod
Multi-view feature learning for VHR remote sensing image classification
Guo, Y., Ji, J., Shi, D., Ye, Q., & Xie, H. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08713-z · Cited by 14
“In the early years, most works are based on the hand-crafted features [2–5, 9, 12, 14, 23, 46] for RS scene classification.”
Background
Research on small target detection algorithm based on improved yolov3
Ye, K., Fang, Z., Huang, X., Ma, X., Ji, J., Wu, Q., & Xie, Y. (2020) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce51767.2020.00321 · Cited by 14
Accurate Instance Segmentation for Remote Sensing Images via Adaptive and Dynamic Feature Learning
Yang, F., Yuan, X., Ran, J., Shu, W., Zhao, Y., Qin, A., & Gao, C. (2021) · Remote Sensing · doi:10.3390/rs13234774 · Cited by 15
Object detection in images using artificial neural network and improved binary gravitational search algorithm
Pourghahestani, F. A., & Rashedi, E. (2015) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis.2015.7391683 · Cited by 14
“Ref [12] developed a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD).”
Similarity-Based Multiple Kernel Learning Algorithms for Classification of Remotely Sensed Images
Niazmardi, S., Safari, A., & Homayouni, S. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2662484 · Cited by 14
LSODNet: A Lightweight and Efficient Detector for Small Object Detection in Remote Sensing Images
Jin, K., Du, W., Tang, M., Liang, W., Li, K., & Pathan, A. K. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3612261 · Cited by 14
“The assessment is conducted using the publicly available NWPU VHR-10 RS dataset [47], [48], as well as two widely used small-object detection datasets, SSDD [49] and LEVIR-Ship [50].”
Fused RetinaNet for small target detection in aerial images
Ahmed, M., Wang, Y., Maher, A., & Bai, X. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2071115 · Cited by 13
“It contains more classes and more small instances than NWPU VHR-10 dataset, as shown in Figure 7.”
MethodInfluential
Adaptive Adjacent Layer Feature Fusion for Object Detection in Remote Sensing Images
Zhang, X., Gong, Z., Guo, H., Liu, X., Ding, L., Zhu, K., & Wang, J. (2023) · Remote Sensing · doi:10.3390/rs15174224 · Cited by 13
LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images
Tian, Y., Wang, X., Zhu, S., Xu, F., & Liu, J. (2023) · Remote Sensing · doi:10.3390/rs15174358 · Cited by 16
“The scenes of NWPU VHR-10 [60] and the Airbus ship dataset [61] are more singular with the coastal background.”
Background
Lightweight high-precision SAR ship detection method based on YOLOv7-LDS
Zhu, S., & Miao, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296992 · Cited by 13
Fixed-precision randomized quaternion singular value decomposition algorithm for low-rank quaternion matrix approximations
Liu, Y., Wu, F., Che, M., & Li, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127490 · Cited by 13
Confidence-Driven Region Mixing for Optical Remote Sensing Domain Adaptation Object Detection
Liu, C., Dong, Y., Zhang, Y., & Li, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3417610 · Cited by 13
“Following [43], we developed two optical remote sensing domain adaptation object detection benchmarks using the NWPU VHR-10 [58] dataset as the source domain and DIOR [20] and HRRSD [59] datasets as the target domains.”
Method
Adaptive Feature Separation Network for Remote Sensing Object Detection
Ma, W., Wu, Y., Zhu, H., Zhao, W., Wu, Y., Hou, B., & Jiao, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3454355 · Cited by 13
“They are NWPU [28], IPIU-M, and DOTA2.”
CUG_MISDataset: A Remote Sensing Instance Segmentation Dataset for Improved Wide-Area High-Precision Mining Land Occupation Recognition
Zhu, Y., Chen, W., He, W., Wang, R., Li, X., & Wang, L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3454333 · Cited by 13
“With the rapid development of deep learning models, progress has been made in the remote sensing classiﬁcation of mining land cover types [6], [7], [8], [9], [10], [11], [12].”
Background
GLFRNet: Global-Local Feature Refusion Network for Remote Sensing Image Instance Segmentation
Zhao, J., Wang, Y., Zhou, Y. Z., Du, W.-L., Yao, R., & Saddik, A. E. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3530515 · Cited by 13
“Three datasets are used to evaluate our model and compare with current popular methods, which are iSAID dataset [30], NWPU VHR-10 dataset [31], and WHU Building dataset [33].”
A Unified Remote Sensing Object Detector Based on Fourier Contour Parametric Learning
Zhang, T., Zhuang, Y., Wang, G., Chen, H., Li, L., & Li, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3540085 · Cited by 13
“Moreover, as shown in the fourth, fifth, and sixth rows of Table IV, the SOTA detectors of STD-O [86], GCL [95], and TIOE-Det [89] in DOTA [51] obtained unremarkable results in NV10 [53], which indicates the instability of OBB parameter regression for objects with extremely high aspect ratios.”
Influential
CAMCFormer: Cross-Attention and Multicorrelation Aided Transformer for Few-Shot Object Detection in Optical Remote Sensing Images
Wang, L., Mei, S., Wang, Y., Lian, J., Han, Z., & Feng, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3543583 · Cited by 13
“This section presents the experimental validation of the proposed algorithm on two public benchmark datasets (NWPU VHR-10 [71] and DIOR [72]) for RSI object detection.”
GSINet: Gradual Semantic Interaction Network for Remote Sensing Object Detection Based on Dual Attention Mechanism
Xu, Y., Qi, Q., He, W. P., Zhang, G., Chen, S., & Tu, B. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3551551 · Cited by 13
“3) NWPU VHR-10 [46]: The NWPU VHR-10 dataset has become a staple in remote sensing research.”
Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images
Qiu, S., Wen, G., Liu, J., Deng, Z., & Fan, Y. (2018) · Remote Sensing · doi:10.3390/rs10030464 · Cited by 13
“For convenience, we take the airplane category as an example to elaborate all related ideas hereinafter.”
Method
Adaptive Anchor Networks for Multi-Scale Object Detection in Remote Sensing Images
Zhang, M., Chen, Y., Liu, X., Lv, B., & Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982658 · Cited by 13
“on two public datasets: DOTA dataset [36] and NWPU VHR-10 dataset [37, 38], both of which have multi-class object annotations.”
Background
FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection
Chen, S., Shao, D., Shu, X., Zhang, C., & Wang, J. (2020) · Electronics · doi:10.3390/electronics9091356 · Cited by 13
Mask R-CNN for Geospatial Object Detection
AL-Alimi, D., Shao, Y., Alalimi, A., & Abdu, A. (2020) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2020.05.05 · Cited by 13
“A number of handcrafted features were proposed in [20, 21, 22, 23], based on OD methods.”
Method
LOVD: Land Vehicle Detection in Complex Scenes of Optical Remote Sensing Image
Yan, P., Liu, X., Wang, F., Yue, C., & Wang, X. Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3133109 · Cited by 13
Remote Sensing Fine-Grained Ship Data Augmentation Pipeline With Local-Aware Progressive Image-to-Image Translation
Liu, B., Li, L., Xiao, Q., Ni, W., & Yang, Z. Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3211517 · Cited by 13
“DOTA [61], NWPU VHR-10 [62], and DSCR [63] contained ship types, which were not enough for ﬁne-grained research.”
Background
RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images
Song, F., Ma, R., Leí, T., & Peng, Z. (2023) · Remote Sensing · doi:10.3390/rs15092364 · Cited by 13
A Lightweight Object Detection and Recognition Method Based on Light Global-Local Module for Remote Sensing Images
Liu, D., Zhang, J., Li, T., Qi, Y., Wu, Y., & Zhang, Y. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3292890 · Cited by 13
“In this letter, several experiments have been conducted on two datasets in the field of remote sensing: the detection in optical remote sensing images (DIOR) dataset [21] and the NWPU-10 very-high-resolution (VHR) dataset [22].”
Method
Super-resolution reconstruction of remote sensing images based on convolutional neural network
Tian, Y., Jia, R., Xu, S., Hua, R., & Deng, M.-D. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.4.046502 · Cited by 14
Subtask Attention Based Object Detection in Remote Sensing Images
Xiong, S., Tan, Y., Li, Y., Wen, C., & Yan, P. (2021) · Remote Sensing · doi:10.3390/rs13101925 · Cited by 13
“Third, we detail the MdS, GMulW and TPA modules and integrate them into StAN, which is then named StAN-Gain”
Influential
From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories
Kazmi, S. M. K. A., Aafaq, N., Khan, M. A., Khalil, M., & Saleem, A. (2023) · IEEE Access · doi:10.1109/access.2023.3299878 · Cited by 13
“Yolo-family object detction models [182], [183], [185] and NWPU-VHR10 [157], DOTA [151] and RSOD [158] datasets were utilized during the study.”
Method
Dual-Stream Global Relationship Learning for Oriented Object Detection in Remote Sensing Images
Sun, P., Zheng, Y., Xu, W., & Yang, J. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3570259 · Cited by 12
“…the effectiveness of the proposed dual-stream global relationship learning (DGRL) method on ﬁve well-known public aerial image datasets, including the OBB detection datasets DOTA [28], HRSC2016 [29], DIOR-R [30], and the horizontal bounding box (HBB) detection datasets NWPU VHR-10 [31], RSOD [32].”
HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images
Zhang, P., Liu, J., Zhang, J., Liu, Y., & Shi, J. (2025) · Remote Sensing · doi:10.3390/rs17152708 · Cited by 12
SAFF-DETR: An End-to-End Object Detection Network for Remote Sensing Images With Targets of Varying Sizes Based on Scale Adaptation and Frequency Fusion
Zhi, Y., Zhao, J., Song, C., Ma, M., & Mei, S. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3618293 · Cited by 12
“A. Datasets 1) NWPU VHR-10 Dataset [61]: The NWPU VHR-10 dataset is an optical remote sensing dataset released by the Northwestern Polytechnical University, which includes 800 ultrahigh resolution (VHR) remote sensing images from Google Earth and Vaihingen databases.”
Lightweight model LMW-YOLO for small object detection in remote sensing images
Qiu, Y., & Lin, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-45055-6 · Cited by 12
Adversarial Remote Sensing Scene Classification Based on Lie Group Feature Learning
Xu, C., Shu, J., & Zhu, G. (2023) · Remote Sensing · doi:10.3390/rs15040914 · Cited by 12
Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement
Zheng, S., Wu, Z., Xu, Y., & Wei, Z. (2024) · Remote Sensing · doi:10.3390/rs16071203 · Cited by 14
Controllable Generative Knowledge-Driven Few-Shot Object Detection From Optical Remote Sensing Imagery
Zhang, T., Zhuang, Y., Wang, G., Chen, H., Wang, H., Li, L., & Li, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3541937 · Cited by 14
“First, to intuitively observe the effects of CMFE for scalable feature learning, some feature maps of detected images from NV10 [13] are visualized in Fig.”
Influential
Self-Supervised Feature Contrastive Learning for Small Weak Object Detection in Remote Sensing
Li, Z., Hu, X., Qian, J., Zhao, T., Xu, D., & Wang, Y. (2025) · Remote Sensing · doi:10.3390/rs17081438 · Cited by 12
A refined methodology for small object detection: Multi-scale feature extraction and cross-stage feature fusion network
Bian, D., Tang, M., Ling, M., Xu, H., Lv, S., Tang, Q., & Hu, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105297 · Cited by 12
MS-IAF: Multi-Scale Information Augmentation Framework for Aircraft Detection
Zhao, Y., Li, J., Li, W., Shan, P., Wang, X., Li, L., & Fu, Q. (2022) · Remote Sensing · doi:10.3390/rs14153696 · Cited by 13
Polycentric Circle Pooling in Deep Convolutional Networks for High-Resolution Remote Sensing Image Recognition
Qi, K., Yang, C., Hu, C., Guan, Q., Tian, W., Shen, S., & Peng, F. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2968564 · Cited by 12
“Additionally, we compare our PCP-net with the BoW feature-based method [48], the spatial sparse coding BoW (SSCBoW) feature-based model [49], the method based on a collection of part detectors (COPD) [46], the RICNN model”
MethodInfluential
Siamese Graph Embedding Network for Object Detection in Remote Sensing Images
Tian, S., Kang, L., Xing, X., Zhou, L., Zhao, L., Fan, C., & Zhang, Y. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.2981420 · Cited by 12
“The NWPU VHR-10 data set contains ten categories of objects.”
MethodInfluential
Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification
Li, F., Feng, R., Han, W., & Wang, L. (2020) · Optics Express · doi:10.1364/oe.395866 · Cited by 12
FPN-GAN: Multi-class Small Object Detection in Remote Sensing Images
Ahmad, T., Chen, X., Saqlain, A. S., & Ma, Y. (2021) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda51879.2021.9442506 · Cited by 12
“Most of the works [26], [27] used handcrafted features in earlier studies, producing very limited detection results.”
Method
Scenario Context-Aware-Based Bidirectional Feature Pyramid Network for Remote Sensing Target Detection
Huang, W., Li, G., Jin, B., Chen, Q., Yin, J., & Huang, L. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3135935 · Cited by 14
Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement
Zhou, L., Li, Y., Rao, X., Liu, C., Zuo, X., & Liu, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2605140 · Cited by 12
HWANet: A Haar Wavelet-based Attention Network for remote sensing object detection
Jin, B., Yin, F., Cai, W., Li, H., Zhu, H., Huang, W., Wu, Q., Chen, H., & Sun, Z. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330759 · Cited by 12
Contextual and orientation correction modules enhance weakly-supervised aerial object detection in remote sensing images
Yang, L., Wang, S., Wang, X., Wang, S., Lu, Y., & Wang, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112110 · Cited by 11
Unsupervised coefficient learning framework for variational pansharpening
Xiao, J.-L., Huang, T., Deng, L.-J., Jiang, H., Zhao, Q., & Vivone, G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103790 · Cited by 11
DMEFF-Net: A Dynamic Multiscale Enhanced Feature Fusion Model for Small Object Detection in Remote Sensing Images
Zhou, G., Yu, L., Gao, E., & Lu, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3634977 · Cited by 11
“To address this issue above, several studies [12], [13], [14], [15] have proposed novel deep learning-based methods, including feature enhancement, multiscale feature fusion, and spatial context awareness.”
YOLO-PICO: Lightweight object recognition in remote sensing images using expansion attention modules
Aghili, M. E., Ghassemian, H., & Imani, M. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113114 · Cited by 11
Remote sensing scene image classification model based on multi-scale features and attention mechanism
Wang, G., Xu, H., Wang, X., Yuan, L., & Wen, X. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.044510 · Cited by 12
Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation
Li, M., Wang, Y., & Sun, H. (2023) · Sensors · doi:10.3390/s23208424 · Cited by 11
“For our experiments the non-uniformity correction for the simulated images was performed using the NWPU VHR-10 dataset [34–36], and the addition of thermal radiation noise was referenced to the degradation model of Shi et al. The BU-TIV dataset [37] was used for the non-uniformity correction of the…”
Method
Improved remote sensing image target detection based on YOLOv7
Xu, S., Chen, Z., Zhang, H., Xue, L., & Su, H. (2024) · Optoelectronics Letters · doi:10.1007/s11801-024-3063-z · Cited by 11
“In this paper, we perform comparison experiments on the NWPU-VHR10 dataset using RICNN, collection of part detectors (COPD) [22] , rotation-insensitive and con-text-augmented object detection (RI-CAO) [23] , and YOLOv5 with the standard YOLOv7 and improved YOLOv7 algorithms, while on the DOTA1.”
A Remote Sensing Target Detection Model Based on Lightweight Feature Enhancement and Feature Refinement Extraction
Guo, D., Zhou, Z., Guo, F., Jia, C., Huang, X., Feng, J., & Shen, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3394887 · Cited by 11
“Among them, SSDD [10], DOTA [11], NWPU [12], and DIOR [13] are the most widely used.”
Background
Few-Shot Incremental Object Detection in Aerial Imagery via Dual-Frequency Prompt
Lü, X., Diao, W., Li, J., Zhang, Y., Wang, P., Sun, X., & Fu, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3400595 · Cited by 11
“2) NWPU VHR-10 [36]: The NWPU VHR-10 dataset comprises 715 high-resolution remote sensing images sourced from Google Earth and 85 pansharpened color infrared images of the Vaihingen dataset [72].”
Method
EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images
Datla, R., Swetha, G., & Gayathri, C. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10134-0 · Cited by 13
An Improved YOLOv8 Detector for Multi-Scale Target Detection in Remote Sensing Images
Yue, M., Zhang, L., Zhang, Y., & Zhang, H. (2024) · IEEE Access · doi:10.1109/access.2024.3444606 · Cited by 12
“In order to validate the performance capabilities of our proposed model and to ascertain its robustness, we carried out extensive experiments on the NWPU VHR-10 dataset [44], [45].”
Method
Multi-Modal Prototypes for Few-Shot Object Detection in Remote Sensing Images
Liu, Y., Pan, Z., Yang, J., Zhou, P., & Zhang, B. (2024) · Remote Sensing · doi:10.3390/rs16244693 · Cited by 15
Self-citation
Semantic Prototyping With CLIP for Few-Shot Object Detection in Remote Sensing Images
Liu, T., Zhou, S., Li, W., Zhang, Y., & Guan, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3550372 · Cited by 12
JM-Net and Cluster-SVM for Aerial Scene Classification
Lu, X., Yuan, Y., & Fang, J. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/332 · Cited by 11
“Recently, aerial image becomes great important for earth observation [Hu et al., 2013; Cheng et al., 2015; Hu et al., 2015; Cheng et al., 2014], Because of the highly complex geometrical structures and spatial patterns, to efficiently understand the semantic information of them is very important,…”
Background
Precise object detection using adversarially augmented local/global feature fusion
Han, X., He, T., Ong, Y.-S., & Zhong, Y. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103710 · Cited by 11
CI-Dataset and DetDSCI Methodology for Detecting Too Small and Too Large Critical Infrastructures in Satellite Images: Airports and Electrical Substations as Case Study
Pérez-Hernández, F., Rodríguez-Ortega, J., Benhammou, Y., Herrera, F., & Tabik, S. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3128994 · Cited by 11
“6m Classification NWPU VHR-10[7] 10 (4) 800 (3651) ∼1000 Google Earth 15cm-12m Horizontal BB xView[16] 60 (9) 1400 (1000000) 3000 DigitalGlobe 31cm Horizontal BB DIOR[17] 20 (11) 23463 (192472) 800 Google Earth 30cm-50cm Horizontal BB DOTA[24] 15 (6) 2806 (188282) 800∼4000 Google Earth 15cm-12m Oriented BB”
Influential
BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation
Liao, N., Guo, B., Li, C., Liu, H., & Zhang, C. (2022) · Remote Sensing · doi:10.3390/rs14184572 · Cited by 11
Optimization of Hyperparameters in Object Detection Models Based on Fractal Loss Function
Ming, Z., Li, B., & Wang, J. (2022) · Fractal and Fractional · doi:10.3390/fractalfract6120706 · Cited by 11
RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images
Zhang, Y., Wang, Y., Zhang, N., Li, Z., Zhao, Z., Gao, Y., Chen, C., & Feng, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3289585 · Cited by 11
“which is the smallest external horizontal rectangular box that can contain the object [1], [2], [3], [4], and the other is oriented bounding box (OBB), which is a rectangular box with corresponding angle according to the rotation direction of the object [3], [5], [6], [7].”
BackgroundMethod
Efficient object proposals extraction for target detection in VHR remote sensing images
Farooq, A., Hu, J., & Jia, X. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127712 · Cited by 11
“Both state-of-the-art methods are evaluated using publicly available target detection NWPU VHR-10 data set [1], [12].”
Method
Airplane detection using convolutional neural networks in a coarse-to-fine manner
Li, X., Wang, S., Jiang, B., & Chan, X. (2017) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec.2017.8284943 · Cited by 11
Few-Shot Object Detection of Remote Sensing Image via Calibration
Li, R., Zeng, Y., Wu, J., Wang, Y., & Zhang, X. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3187566 · Cited by 10
LTEA-YOLO: An Improved YOLOv5s Model for Small Object Detection
Li, B., Huang, S., & Zhong, G. (2024) · IEEE Access · doi:10.1109/access.2024.3429282 · Cited by 10
“NWPU VHR-10 [32] belongs to the open remote sensing small target dataset, including 10 categories: airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle.”
Background
Multi-level domain perturbation for source-free object detection in remote sensing images
Liu, W., Liu, J., Su, X. B., Nie, H., & Luo, B. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2378920 · Cited by 10
“…leverage a synthetic dataset GTAV10k (Liu, Liu, and Luo 2023) and six real datasets for remote sensing image object detection: xView (Lam et al. 2018), NWPU VHR-10 (Cheng et al. 2014), DIOR (Li et al. 2020), UCAS-AOD (Zhu et al. 2015), ITCVD (Yang et al. 2019) and DLR-3K (K. Liu and Mattyus 2015).”
LEN-YOLO: a lightweight remote sensing small aircraft object detection model for satellite on-orbit detection
Wu, J., Zhao, F., & Jin, Z. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01601-x · Cited by 11
“To assess the LEN-YOLO model’s performance on TX2 and its generalizability, the proposed approach is applied to the NWPUVHR [59] and UCAS-AOD [60] datasets.”
Detection of small objects in remote sensing images based on bi-level routing attention and deformable convolution
Chen, P., Liu, S., Feng, W., Wang, H., & Li, X. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105045 · Cited by 10
LLaMA-Unidetector: An LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery
Wang, G., Xie, J., Zhang, T., Sun, Y., Chen, H., Zhuang, Y., & Li, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3564332 · Cited by 15
“1) NWPU-VHR10: The NWPU-VHR10 [33] dataset is a widely used benchmark for object detection in high-resolution remote sensing images.”
Importance-Aware Robust Semantic Transmission for LEO Satellite–Ground Communication
Hui, C., Meng, R., Xu, X., Han, S., & Zhang, P. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3645361 · Cited by 10
“To further validate the generalizability of the proposed model, we incorporate the NWPU VHR-10 dataset [32] as an auxiliary benchmark.”
Scale-Aware Anchor-Free Object Detection via Curriculum Learning for Remote Sensing Images
Cai, W., Zhang, B., & Wang, B. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3115796 · Cited by 10
High Quality Object Detection for Multiresolution Remote Sensing Imagery Using Cascaded Multi-Stage Detectors
Wu, B., Shen, Y., Guo, S., Chen, J., Sun, L., Li, H., & Ao, Y. (2022) · Remote Sensing · doi:10.3390/rs14092091 · Cited by 10
“It is worth noting that other recent studies explored improvements from different perspectives (e.g., feature pyramids), suggesting that the cumulative effect of these enhancements can further improve the performance of multi-stage detectors in the fields of remote sensing object detection, instance segmentation, key…”
MethodInfluential
Few-Shot Aircraft Detection in Satellite Videos Based on Feature Scale Selection Pyramid and Proposal Contrastive Learning
Zhou, Z., Li, S., Guo, W., & Gu, Y. (2022) · Remote Sensing · doi:10.3390/rs14184581 · Cited by 10
Diversity Measurement-Based Meta-Learning for Few-Shot Object Detection of Remote Sensing Images
Wang, L., Zhang, S., Han, Z., Yan, F., Jiang, W., & Mei, S. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884721 · Cited by 10
“NWPU VHR-10 [1], RSOD [2], and DIOR [3]) usually have far less numbers of images.”
Background
Multi-Branch Parallel Networks for Object Detection in High-Resolution UAV Remote Sensing Images
Wu, Q., Zhang, B., Guo, C., & Wang, L. (2023) · Drones · doi:10.3390/drones7070439 · Cited by 10
Attention-based feature pyramid networks for ship detection of optical remote sensing image
Yu, Y., Hua, A. I., He, X., Shuhai, Y. U., Xing, Z., & Zhu, R. (2020) · National Remote Sensing Bulletin · doi:10.11834/jrs.20208264 · Cited by 13
Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features
Wang, B., Ma, G., Sui, H., Zhang, Y., Zhang, H., & Zhou, Y. (2023) · Remote Sensing · doi:10.3390/rs15143462 · Cited by 10
Boundary-aware small object detection with attention and interaction
Feng, Q., Shao, Z., & Wang, Z. (2023) · The Visual Computer · doi:10.1007/s00371-023-03144-x · Cited by 10
“We validate BA 2 SNet on two major datasets, including VisDrone2019-DET [14] and NWPU VHR-10 [26].”
A Multiscale Information Fusion Network Based on PixelShuffle Integrated With YOLO for Aerial Remote Sensing Object Detection
Li, X., Hou, J. W., Lin, G., Hei, Y. Q., & Li, W. T. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3353304 · Cited by 10
“In order to testify to the efficiency of the proposed algorithm, three classic aerial remote sensing datasets, i.e., MAR20 [12], NWPU VHR-10 [13], and RSOD [14] are selected and employed in our experiment.”
Method
Deep multiple instance learning for airplane detection in high-resolution imagery
Mohammadi, M. R. (2021) · Machine Vision and Applications · doi:10.1007/s00138-020-01153-7 · Cited by 11
A subclass supported convolutional neural network for object detection and localization in remote-sensing images
Kılıç, E., & Öztürk, S. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1562260 · Cited by 10
“Zhang et al. (2014) and Cheng et al. (2014) used part-based models for object detection in high-resolution remote-sensing images.”
Method

Authors: Cheng, G., Han, J., Zhou, P., & Guo, L.
Affiliations: Northwestern Polytechnical University (CN)
Corresponding author: Han, Junwei
ORCID: Cheng, Gong
Han, Junwei
Zhou, Peicheng
Guo, Lei
Venue: ISPRS Journal of Photogrammetry and Remote Sensing
Publisher: Elsevier BV
Published: 2014-12
Volume: 98
Pages: 119-132
ISSN: 0924-2716 (ISSN-L)
DOI: 10.1016/j.isprsjprs.2014.10.002
OpenAlex: W2005368619
Cited by: 1,007
References: 38
Citations by year: 2026: 115 · 2025: 152 · 2024: 141 · 2023: 111 · 2022: 105 · 2021: 105 · 2020: 87 · 2019: 64 · 2018: 41 · 2017: 44 · 2016: 26 · 2015: 16
FWCI: 43.77
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
Funders: Ministry of Education of the People's Republic of China (20136102110037)
National Natural Science Foundation of China (61401357, 61333017, 61473231, 91120005)
China Postdoctoral Science Foundation (2014M552491)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: geospatial object detection, linear support vector machine, spatial pattern detection, remote sensing images, image representation

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

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