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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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Cites
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Histograms of Oriented Gradients for Human Detection
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The pascal visual object classes (VOC) challenge
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Visual categorization with bags of keypoints
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A Bayesian Hierarchical Model for Learning Natural Scene Categories
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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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Ensemble of exemplar-SVMs for object detection and beyond
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865
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Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey
Ullah, F., Ullah, I., Khan, R. U., Khan, S., Khan, K., & Pau, G. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3353551 · Cited by 120
“These local descriptors are often aggregated into bag-of-visual-words (BoVW) models [49], [53], [85], [90], [91], [92], [93], [94], [95] and HOG-feature-based models [47], [96].”
MethodFeng, X., Han, J., Yao, X., & Cheng, G. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2985989 · Cited by 113
“as the collection of part detector(COPD) [50], the transferred CNN model from AlexNet [7], the RICNN [30], RCNN [51], Fast-RCNN [46], and Faster-RCNN [52].”
MethodSelf-citationTCANet: Triple Context-Aware Network for Weakly Supervised Object Detection in Remote Sensing Images
Feng, X., Han, J., Yao, X., & Cheng, G. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3030990 · Cited by 114
Self-citationCross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery
Li, Y., Huang, Q., Pei, X., Chen, Y., Jiao, L., & Shang, R. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3046482 · Cited by 120
“NWPUVHR-10: [45], [46] dataset is a publicly remote sensing dataset for research purposes only, covering ten categories: ship, baseball-diamond (BD), airplane, tennis-court (TC), ST, GTF, vehicle, harbor, BC, and bridge.”
BackgroundHan, W., Chen, J., Wang, L., Feng, R., Li, F., Wu, L., Tian, T., & Yan, J. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3041450 · Cited by 119
Remote Sensing Image Scene Classification Using Rearranged Local Features
Yuan, Y., Fang, J., Lu, X., & Feng, Y. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2869101 · Cited by 112
“During the past decades, remarkable efforts have been made for the task of remote sensing image scene classification due to its important role for a wide range of applications [1]–[3].”
BackgroundFeature Enhancement Network for Object Detection in Optical Remote Sensing Images
Cheng, G., Lang, C., Wu, M., Xie, X., Yao, X., & Han, J. (2021) · Journal of Remote Sensing · doi:10.34133/2021/9805389 · Cited by 114
“Traditional methods like Histogram of Oriented Gradients (HOG) [9, 10] and Scale Invariant Feature Transform (SIFT) [11] require careful manual engineering and a large amount of time when faced with considerable data examples.”
MethodSelf-citationThe Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification
Zhao, B., Zhong, Y., Zhang, L., & Huang, B. (2016) · Remote Sensing · doi:10.3390/rs8020157 · Cited by 110
“To bridge the semantic gap, scene classification methods based on the bag-of-visual-words (BOVW) model [13–18], part detectors [19,20], and neural networks [21–23] have been proposed, among which the BOVW model is one of the most popular approaches.”
MethodA Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery
Kang, J., Tariq, S., Oh, H., & Woo, S. S. (2022) · IEEE Access · doi:10.1109/access.2022.3149052 · Cited by 121
“In this work, we propose two promising research directions based on the comprehensive survey of deep learning-based methods and overhead imagery datasets.”
MethodScale Adaptive Proposal Network for Object Detection in Remote Sensing Images
Zhang, S., He, G., Chen, H., Jing, N., & Wang, Q. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2888887 · Cited by 106
Chen, J., Li, W., Zhu, J., Xu, G., & Deng, M. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2930462 · Cited by 107
“sets, NWPU VHR-10 [21] and DOTA [22], were used to evaluate the proposed MSCA mechanism.”
MethodScene classification of high resolution remote sensing images using convolutional neural networks
Cheng, G., Ma, C., Zhou, P., Yao, X., & Han, J. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729193 · Cited by 110
“However, most of them are based on handcrafted or shallow learning-based features, such as bag-of-visual-words (BOVW) model [23] and its variations [4, 20, 21], sparse coding-based features [12, 19], midlevel visual elements-oriented features [9, 11, 15, 18] and so on.”
BackgroundSelf-citationFine-Grained Recognition for Oriented Ship Against Complex Scenes in Optical Remote Sensing Images
Han, Y., Yang, X., Pu, T., & Peng, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3123666 · Cited by 108
Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., & Wang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00129 · Cited by 99
“Aerial Image-based Object Detection A few aerial image datasets ( i.e. DOTA [52], NWPU VHR-10 [9], and VEDAI [38] ) were proposed recently.”
BackgroundA Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform
Han, Y., Liu, H., Wang, Y., & Liu, C. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3216564 · Cited by 105
“, DOTA [49], NWPU VHR-10 [50], and VEDAI [51]).”
BackgroundZhou, P., Cheng, G., Liu, Z., Bu, S., & Hu, X. (2015) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-015-0370-3 · Cited by 96
“Finally, a post-processing scheme is used to eliminate repeated detections via non-maximum suppression (Cheng et al. 2013b, 2014; Han et al. 2015a, 2014; Zhang et al. 2015).”
MethodInfluentialSelf-citationYao, X., Han, J., Guo, L., Bu, S., & Liu, Z. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.073 · Cited by 94
Self-citationFusion of block and keypoints based approaches for effective copy-move image forgery detection
Zheng, J., Liu, Y., Ren, J., Zhu, T., Yan, Y., & Yang, H. (2016) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-016-0416-1 · Cited by 93
“To determine the shortest distance between two keypoints, existing approach is to set a threshold t d , where only if the Euclidean distance between the two keypoints is greater than t d the keypoints will be used in comparison (Cheng et al. 2015, 2014).”
MethodZhang, X., Zhu, K., Chen, G., Tan, X., Zhang, L., Dai, F., Liao, P., & Gong, Y. (2019) · Remote Sensing · doi:10.3390/rs11070755 · Cited by 92
“Another common geospatial object detection dataset is NWPU VHR-10 [44], which contains 800 images in 10 categories with a total of 3651 instances.”
BackgroundInfluentialMei, S., Jiang, R., Ma, M., & Song, C. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3233726 · Cited by 93
“Illustration of the rotation variance problem of remote-sensing objects, from the NWPU-VHR-10 dataset [11].”
BackgroundMethodDevelopment and Application of Ship Detection and Classification Datasets: A review
Zhang, C., Zhang, X., Gao, G., Lang, H., Liu, G., Cao, C., Song, Y., Guan, Y., & Dai, Y. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3450681 · Cited by 91
“Leveraging the ORS images from Google Earth, researchers established several large remote sensing target detection datasets, including NWPUVHR-10 [41], DOTA [42], HRRSD [43], LEVIR [44], xView [45], and DIOR [46].”
UAVs meet LLMs: Overviews and perspectives towards agentic low-altitude mobility
Tian, Y., Lin, F., Li, Y., Zhang, T., Zhang, Q., Fu, X., Huang, J., Dai, X., Wang, Y., Tian, C., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103158 · Cited by 139
Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery
Gong, Y., Xiao, Z., Tan, X., Sui, H., Xu, C., Duan, H., & Li, D. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2930246 · Cited by 93
“The experimental results on the NWPU VHR-10 data set show a great improvement in object detection performance.”
MethodInfluentialRekavandi, A. M., Xu, L., Boussaïd, F., Seghouane, A., Hoefs, S., & Bennamoun, M. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3530678 · Cited by 119
“NWPU VHR-10 [236] is a high spatial resolution remote sensing image dataset containing 10 classes of objects (air-planes, ships, storage tanks, baseball diamonds, tennis courts, basketball courts, ground track fields, harbors, bridges, and vehicles).”
Deep learning-based small object detection: A survey
Feng, Q., Xu, X., & Wang, Z. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023282 · Cited by 101
“NWPU VHR-10 [167] 2016 The NWPU VHR-10 dataset contains a total of 800 very high-resolution optical remote sensing images, which were acquired from Google Earth and Vaihingen.”
BackgroundPoint-Based Estimator for Arbitrary-Oriented Object Detection in Aerial Images
Fu, K., Chang, Z., Zhang, Y., & Sun, X. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3020165 · Cited by 87
“proposed to advance object detection in aerial images [5], [7],”
BackgroundRajagopal, A., Joshi, G. P., Ramachandran, A., Subhalakshmi, R. T., Khari, M., Jha, S., Shankar, K., & You, J. (2020) · IEEE Access · doi:10.1109/access.2020.3011502 · Cited by 83
Lin, H., Shi, Z., & Zou, Z. (2017) · Remote Sensing · doi:10.3390/rs9050480 · Cited by 81
“Aside from detecting scattered objects, the classification of scenes also receives a lot of attention recently, such as in [15–17], where the objective is to classify image patches into different classes, such as buildings, forest, harbor, etc.”
BackgroundChen, J., Chen, K., Chen, H., Zou, Z., & Shi, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3180894 · Cited by 87
“NWPU-VHR-10 [65] contains 800 images in 10 categories.”
BackgroundOriented object detection in optical remote sensing images using deep learning: a survey
Wang, K., Wang, Z., Li, Z., Su, A., Teng, X., Pan, E., Liu, M., & Yu, Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11256-0 · Cited by 103
“, 2020), LEVIR (Zou and Shi, 2018), NWPU VHR-10 (Cheng et al., 2014), RSOD (Xiao et al.”
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource DataPreprint
Li, H., Dou, X., Tao, C., Hou, Z., Chen, J., Peng, J., Deng, M., & Zhao, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10450 · Cited by 79
“convolution neural network1 Introduction Remote sensing image classification is a fundamental task in remote sensing image processing and there has great meaningful work been done(Cheng et al., 2015; Cheng et al., 2014; Cheng et al., 2016; Han et al., 2015; Yao et al., 2016; Yao et al., 2017). Deep convolution neural…”
BackgroundRecurrent Thrifty Attention Network for Remote Sensing Scene Recognition
Fu, L., Zhang, D., & Ye, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3042507 · Cited by 77
“COMPARISONS WITH STATE-OF-THE-ART METHODS ON THE TEST SET OF NWPU VHR-10 [27]”
BackgroundMethodInfluentialCultivated land use efficiency and its driving factors in the Yellow River Basin, China
Zhou, X., Wu, D., Li, J., Liang, J., Zhang, D., & Chen, W. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109411 · Cited by 77
Global to Local: Clip-LSTM-Based Object Detection From Remote Sensing Images
Teng, Z., Duan, Y., Liu, Y., Zhang, B., & Jianping Fan, B. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3064840 · Cited by 75
Laplacian Feature Pyramid Network for Object Detection in VHR Optical Remote Sensing Images
Zhang, W., Jiao, L., Li, Y., Huang, Z., & Wang, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3072488 · Cited by 76
“NL-LFPN-MR101 outperforms FDDL [46], SSCBoW [13], COPD [18], RICNN [1], RICAOD [7], and FPN-MR101 with 68.49%, 62.42%, 38.65%, 20.60%, 6.11%, and 3.51%, respectively.”
BackgroundMethodInfluentialYOLO-DCTI: Small Object Detection in Remote Sensing Base on Contextual Transformer Enhancement
Min, L., Fan, Z., Lv, Q., Reda, M., Shen, L., & Wang, B. (2023) · Remote Sensing · doi:10.3390/rs15163970 · Cited by 77
“Furthermore, to assess the algorithm’s overall performance, this experiment included training and testing on the VISDrone dataset [50] and NWPU VHR-10 datasets [51].”
MethodSRAF-Net: Shape Robust Anchor-Free Network for Garbage Dumps in Remote Sensing Imagery
Sun, X., Liu, Y., Yan, Z., Wang, P., Diao, W., & Fu, K. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3023928 · Cited by 73
YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Detection in Aerial Imagery
Betti, A., & Tucci, M. (2023) · Sensors · doi:10.3390/s23041865 · Cited by 81
“By also estimating more robust anchors by means of k-means clustering based on Jake’s distance, on the NWPU VHR-10 dataset [22,23], TF-YOLO outperforms Tiny-YOLOv3 with a mean Average Precision (mAP) almost 6% higher and a similar speed of roughly 24 Frames Per Second (FPS).”
MethodRan, Q., Wang, Q., Zhao, B., Wu, Y., Pu, S., & Li, Z. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3079968 · Cited by 73
“The NWPU VHR-10 dataset [38] is derived from a 10-level geographic remote sensing dataset for space object detection.”
MethodFew-Shot Object Detection of Remote Sensing Images via Two-Stage Fine-Tuning
Zhao, Z., Tang, P. T. P., Zhao, L., & Zhang, Z. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3116858 · Cited by 72
SRAF-Net: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images
Liu, J., Li, S., Zhou, C., Cao, X., Gao, Y., & Wang, B. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3124959 · Cited by 74
IDeRs: Iterative dehazing method for single remote sensing image
Xu, L., Zhao, D., Yan, Y., Kwong, S., Chen, J., & Duan, L. (2019) · Information Sciences · doi:10.1016/j.ins.2019.02.058 · Cited by 71
Li, Z., Wang, Y., Xu, D., Gao, Y., & Zhao, T. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110976 · Cited by 71
Mookambiga, A., & Gomathi, V. (2016) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-016-0415-2 · Cited by 71
“Some of the applications of pansharpening include change detection using temporal datasets, the enhancement of specific features and enhancing classification (Cheng et al. 2014).”
Aircraft detection in remote sensing image based on corner clustering and deep learning
Liu, Q., Xiang, X., Wang, Y., Luo, Z., & Fang, F. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103333 · Cited by 70
Li, Y., Huang, X., & Liu, H. (2017) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.83.8.567 · Cited by 69
Explainable Artificial Intelligence for Developing Smart Cities Solutions
Thakker, D., Mishra, B. K., Abdullatif, A., Mazumdar, S., & Simpson, S. (2020) · Smart Cities · doi:10.3390/smartcities3040065 · Cited by 75
Multistage Enhancement Network for Tiny Object Detection in Remote Sensing Images
Zhang, T., Zhang, X., Zhu, X., Wang, G., Han, X., Tang, X., & Jiao, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3363614 · Cited by 74
“Later, machine learning approaches became mainstream in RSOD [19], [20], [21].”
BackgroundMethodA Relation-Augmented Embedded Graph Attention Network for Remote Sensing Object Detection
Tian, S., Kang, L., Xing, X., Tian, J., Fan, C., & Zhang, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3073269 · Cited by 72
“According to feature types designed for detectors, the object detection approaches can be categorized into traditional approach and convolutional neural networks (CNNs)-based approach [2].”
MethodHuang, H. B., & Xu, K. (2019) · Remote Sensing · doi:10.3390/rs11141687 · Cited by 67
“Therefore, sub-meter level HSRRS images have been applied in many areas such as land resources planning [9,10], geospatial object detection [11–13], and environmental monitoring [14].”
MethodDKTNet: Dual-Key Transformer Network for small object detection
Xu, S., Gu, J., Hua, Y., & Liu, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.055 · Cited by 69
A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification
Yu, H., Yang, W. T., Xia, G.-S., & Liu, G. (2016) · Remote Sensing · doi:10.3390/rs8030259 · Cited by 67
Khan, S. D., Alarabi, L., & Basalamah, S. M. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06288-x · Cited by 68
Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model
Ye, Z., Wei, J., Lin, Y., Guo, Q., Zhang, J., Zhang, H., Deng, H., & Yang, K. (2022) · Remote Sensing · doi:10.3390/rs14061523 · Cited by 66
Cheng, G., Zhou, P., Han, J., Guo, L., & Han, J. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0270 · Cited by 67
Self-citationWang, M., Cui, Q., Sun, Y., & Wang, Q. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.010 · Cited by 68
Wang, J., Luo, C., Huang, H., Zhao, H., & Wang, S. (2017) · Remote Sensing · doi:10.3390/rs9030225 · Cited by 65
Deep Feature Correlation Learning for Multi-Modal Remote Sensing Image Registration
Quan, D., Wang, S., Gu, Y., Lei, R., Yang, B., Wei, S., Hou, B., & Jiao, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3187015 · Cited by 65
RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network
Mo, H., & Zhao, G. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109994 · Cited by 80
Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection
Lu, X., Zhang, Y., Yuan, Y., & Feng, Y. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2935177 · Cited by 62
“[16] proposed a multilevel HOG feature pyramid called the collection of part detectors (COPD).”
BackgroundMethodRegion-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images
Lei, J., Luo, X., Fang, L., Wang, M., & Gu, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2968802 · Cited by 63
“Many hand-crafted features were earlier used in object detection [11]–[13].”
BackgroundMethodInfluentialMultiple Instance Graph Learning for Weakly Supervised Remote Sensing Object Detection
Wang, B., Zhao, Y., & Li, X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3123231 · Cited by 62
CA-YOLO: Model Optimization for Remote Sensing Image Object Detection
Shen, L., Lang, B., & Song, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3290480 · Cited by 68
“For the experimental detection benchmark, model training and testing were conducted on open datasets with increasing scale, namely RSOD [33], NWPU VHR-10 [34], and DOTA.”
MethodLearning coarse-to-fine sparselets for efficient object detection and scene classification
Cheng, G., Han, J., Guo, L., & Liu, T. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298721 · Cited by 62
“In recent years, since the seminal work of [4, 5] and with the success of deformable part models (DPMs) [6, 7], part model-based methods have demonstrated impressive performance for object detection [6-13] and scene classification [14-21].”
BackgroundMethodInfluentialSelf-citationChen, S., Zhan, R., & Zhang, J. (2018) · Remote Sensing · doi:10.3390/rs10060820 · Cited by 62
Vehicle detection of multi-source remote sensing data using active fine-tuning network
Wu, X., Li, W., Hong, D., Tian, J., Tao, R., & Du, Q. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.06.016 · Cited by 60
“, NWPU VHR-10 dataset (Cheng et al., 2014; Cheng et al., 2016; Cheng and Han, 2016),(1) DOTA dataset (Xia et al.”
BackgroundZhang, Y., Liu, T., Yu, P., Wang, S., & Tao, R. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387572 · Cited by 59
“2) NWPU VHR-10 Dataset [58]: The NWPU VHR-10 dataset includes 800 high-resolution images selected from Google Earth and the Vaihingen databases, of which 650 images contain objects and the remaining 150 images do not contain objects.”
MethodObject Detection Using Convolutional Neural Networks in a Coarse-to-Fine Manner
Li, X., & Wang, S. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2749478 · Cited by 59
Zhang, J., Lu, C., Wang, J., Yue, G. X.-G., Lim, S., Al-Makhadmeh, Z., & Tolba, A. K. R. (2020) · Sensors · doi:10.3390/s20041188 · Cited by 58
“Low-level visual features include the scale-invariant feature transform (SIFT) [7], histogram of oriented gradients (HOG) [8], gray-level co-occurrence matrix (GLCM), etc.”
BackgroundKim, J., & Chi, M. (2021) · Remote Sensing · doi:10.3390/rs13132532 · Cited by 59
Learning to Holistically Detect Bridges From Large-Size VHR Remote Sensing Imagery
Li, Y., Luo, J., Zhang, Y., Tan, Y., Yu, J.-G., & Bai, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3393024 · Cited by 63
“For example, NWPU VHR-10 [5] is a dataset with ten categories, expanding the category of geospatial objects.”
BackgroundStewart, A. J., Robinson, C., Corley, I., Ortiz, A., Ferres, J. L., & Banerjee, A. (2022) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3557915.3560953 · Cited by 80
Weakly Supervised Rotation-Invariant Aerial Object Detection Network
Feng, X., Yao, X., Cheng, G., & Han, J. (2022) · 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/cvpr52688.2022.01375 · Cited by 56
“Noting that we obtain superior performance to some fully supervised approaches, such as COPD [1], Transferred CNN [21], RCNN [12].”
MethodSelf-citationZhang, K., & Shen, H. (2022) · Remote Sensing · doi:10.3390/rs14030579 · Cited by 55
“Among them, some typical remote sensing image datasets include DOTA (a large-scale Dataset for Object deTection in Aerial images) [38], UCAS-AOD (UCAS-High Resolution Aerial Object Detection Dataset) [39], NWPU VHR-10 (NWPU very-high resolution optical remote sensing images with 10 categories) [1,21,40], RSOD-Dataset…”
BackgroundA new land-cover match-based change detection for hyperspectral imagery
Seydi, S. T., & Hasanlou, M. (2017) · European Journal of Remote Sensing · doi:10.1080/22797254.2017.1367963 · Cited by 55
“…in various areas of Earth science studies, including object detection (Cheng & Han, 2016; Cheng, Zhou, & Han, 2016), image classification (Cheng, Han, Zhou, & Guo, 2014; Cheng et al., 2015; Hassanzadeh & Karami, 2016; Raczko & Zagajewski, 2017), anomaly detection (AD) (L. Zhang & Zhao,…”
Romaszewski, M., Głomb, P., & Cholewa, M. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.08.011 · Cited by 56
Zhang, T., Zhang, X., Zhu, P., Tang, X., Li, C., Jiao, L., & Zhou, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3096185 · Cited by 53
“This is mainly due to the characteristics of the NWPU VHR-10 Instance Segmentation dataset.”
MethodInfluentialM-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework
Yang, Y., Zhuang, Y., Bi, F., Shi, H., & Xie, Y. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2708722 · Cited by 53
“The first data set was the NWPU VHR-10 data set [13], which contains 80 airport images with dimensions of approximately 900 pixels × 800 pixels.”
MethodRAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL
Wu, Z., Chen, X., Gao, Y., & Li, Y. (2018) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-1915-2018 · Cited by 53
SKNet: Detecting Rotated Ships as Keypoints in Optical Remote Sensing Images
Cui, Z., Leng, J., Liu, Y., Zhang, T., Quan, P., & Zhao, W. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3053311 · Cited by 55
“In comparison, the quantity and quality of the ship in HPDM-OSOD are greatly higher than the analogous data sets [17], [42], [43].”
ResultCANet: Centerness-Aware Network for Object Detection in Remote Sensing Images
Shi, L., Kuang, L., Xu, X., Pan, B., & Shi, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3068970 · Cited by 53
“1) Data Sets: In the experiment, our proposed network is validated on NWPU very-high-resolution (VHR)-10 [43] and RSOD [44].”
MethodHuang, X. Q., He, B., Tong, M., Wang, D., & He, C. (2021) · Remote Sensing · doi:10.3390/rs13193816 · Cited by 52
“NWPU VHR-10 [28] contains 800 VHR optical remote sensing images manually annotated by experts.”
BackgroundMethodFew-Shot Object Detection in Aerial Imagery Guided by Text-Modal Knowledge
Lü, X., Sun, X., Diao, W., Mao, Y., Li, J., Zhang, Y., Wang, P., & Fu, K. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3250448 · Cited by 53
“Thus, multiple experts in the field of remote sensing are invited to artificially summarize the descriptions of categories in three datasets [2], [3], [13] for subsequent research, as shown in the Appendix.”
BackgroundMethodInfluentialLand Use and Land Cover Classification with Deep Learning-Based Fusion of SAR and Optical Data
Irfan, A., Li, Y., Xinhua, E., & Sun, G. (2025) · Remote Sensing · doi:10.3390/rs17071298 · Cited by 66
Lv, Y., Zhang, X., Xiong, W., Cui, Y., & Cai, M. (2019) · Remote Sensing · doi:10.3390/rs11243006 · Cited by 52
Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., & Jin, Y. (2020) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20201303.5524 · Cited by 51
“1 Support Vector Machine (SVM) SVM is a non-parametric supervised statistical learning classifier and has become increasingly popular for remote sensing classification([34-36]).”
BackgroundLearning scene-vectors for remote sensing image scene classification
Datla, R., Perveen, N., & Mohan, C. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127679 · Cited by 51
IORN: An Effective Remote Sensing Image Scene Classification Framework
Wang, J., Liu, W., Ma, L., Chen, H., & Chen, L. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2859024 · Cited by 51
“Consequently, remote sensing image scene classification has been receiving increased attention [1], [5], [6].”
BackgroundSong, S., Liu, J., Liu, Y., Feng, G., Han, H., Yao, Y., & Du, M. (2020) · Sensors · doi:10.3390/s20020397 · Cited by 53
Jiang, B., An, X., Xu, S., & Chen, Z. (2022) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-022-01496-w · Cited by 58
Liu, Y., Pan, Z., Yang, J., Zhang, B., Zhou, G., Hu, Y., & Ye, Q. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3369666 · Cited by 56
“The object classes in the DIOR dataset include airplane, airport, baseball field, basketball court, bridge, NWPU VHR-10 is a very high-resolution (VHR) optical remote-sensing dataset released by Cheng et al. [52].”
BackgroundMethodConcentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification
Qi, K., Guan, Q., Yang, C., Peng, F., Shen, S., & Wu, H. (2018) · Remote Sensing · doi:10.3390/rs10060934 · Cited by 50
Chen, J., Hong, H., Song, B., Guo, J. J., Chen, C., & Xu, J. (2023) · Remote Sensing · doi:10.3390/rs15020371 · Cited by 49
EMSCNet: Efficient Multisample Contrastive Network for Remote Sensing Image Scene Classification
Zhao, Y., Liu, J., Yang, J., & Wu, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3262840 · Cited by 49
“A. Remote Sensing Image Scene Classification 1) Handcrafted Features Methods: The early handcrafted feature-based methods mainly extracted color, texture, and edges from images as features [17], [18], [19].”
MethodSingle satellite image dehazing via linear intensity transformation and local property analysis
Ni, W., Gao, X., & Wang, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.010 · Cited by 49
Zhang, T., Gao, P., Dong, H., Zhuang, Y., Wang, G., Zhang, W., & Chen, H. (2022) · Remote Sensing · doi:10.3390/rs14225675 · Cited by 49
Accurate non-maximum suppression for object detection in high-resolution remote sensing images
Qiu, S., Wen, G., Deng, Z., Liu, J., & Fan, Y. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1415473 · Cited by 48
“In this letter, we run experiments on the NWPU10 dataset (Cheng et al. 2014) which has 10 classes: airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbour, bridge and vehicle.”
BackgroundMethodInfluentialMulti-Scale Image Block-Level F-CNN for Remote Sensing Images Object Detection
Zhao, W., Ma, W., Jiao, L., Chen, P., Yang, S., & Hou, B. (2019) · IEEE Access · doi:10.1109/access.2019.2908016 · Cited by 48
“For processing NWPU VHR-10 data set in batches, the size of all images is changed to 1024 × 1024.”
BackgroundMethodInfluentialA Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection
Zhang, J., Xie, C., Xu, X., Shi, Z., & Pan, B. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3015049 · Cited by 48
“Two public data sets, namely, NWPU [51], RSOD [52], and DIOR [53], are used for comparison.”
MethodZhang, F., Shi, Y., Xiong, Z., & Zhu, X. X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3347329 · Cited by 53
“We evaluate the proposed method on three large-scale public OD datasets in remote sensing, including NWPU-VHR10 v2 [51], DIOR [52], and instance segmentation in serial images dataset (iSAID) [21].”
Method
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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