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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Bag-of-visual-words and spatial extensions for land-use classification
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Boosting Object Detectors via Strong-Classification Weak-Localization Pretraining in Remote Sensing Imagery
Zhang, C., Liu, T., Xiao, J., Lam, K., & Wang, Q. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3315392 · Cited by 46
“In order to extensively evaluate the proposed pretraining methods, we leverage our large-scale RS pretraining dataset for upstream pretraining and three public RS object detection datasets for downstream fine-tuning, namely, DOTA [29], DIOR [28], and NWPU VHR-10 [14], [30] in the experiments.”
Influential
DTCNet: Transformer-CNN Distillation for Super-Resolution of Remote Sensing Image
Lin, C., Mao, X., Qiu, C., & Zou, L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3409808 · Cited by 48
“In this section, experiments were undertaken to verify the efﬁcacy and versatility of the proposed model using four publicly available remote sensing image datasets: 1) NWPU-RESISC45 [38], 2) UCMerced [39], 3) AID [40], and 4) NWPU VHR-10 [41].”
Method
Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes
Zhai, D., Dong, J., Cadisch, G., Wang, M., Kou, W., Xu, J., Xiao, X., & Abbas, S. (2017) · Remote Sensing · doi:10.3390/rs10010044 · Cited by 46
“Second, we assumed an object-based approach could improve the accuracy of the pixel-based phenology approach in the previous study [19] for mountainous Xishuangbanna area, as the object-related approach has been proven useful in identification large-scale artificial objects [24-26] e.”
Method
Scene classification for aerial images based on CNN using sparse coding technique
Qayyum, A., Malik, A. S., Saad, M. N. M., Iqbal, M., Abdullah, M. F., Rasheed, W., Abdullah, T. A. R. B. T., & Jafaar, M. Y. B. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1296206 · Cited by 47
“Cheng et al. (2014) proposed a rotation-invariant model that extracted discriminative image patches by using a collection of partial detectors.”
Background
A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images
Ma, W., Guo, Q., Wu, Y., Zhao, W., Zhang, X., & Jiao, L. (2019) · Remote Sensing · doi:10.3390/rs11070737 · Cited by 47
“In the implementation of the ten-class object detection task, the COPD is made up of 45 seed-based part detectors.”
MethodInfluential
Improved YOLOv5 network method for remote sensing image-based ground objects recognition
Xue, J., Shandong University of Science and Technology, Dongye, C., Dong-Ye, C., Wang, P., & Yasir, M. (2022) · Soft Computing · doi:10.1007/s00500-022-07106-8 · Cited by 49
Remote sensing scene classification based on rotation-invariant feature learning and joint decision making
Zhou, Y., Liu, X., Zhao, J., Ma, D., Yao, R., Liu, B., & Zheng, Y. (2019) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0398-z · Cited by 45
Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images
Wang, Y., Xu, C., Liu, C., & Li, Z. (2022) · Remote Sensing · doi:10.3390/rs14143255 · Cited by 45
Dual Learning-Based Graph Neural Network for Remote Sensing Image Super-Resolution
Liu, Z., Feng, R., Wang, L., Han, W., & Zeng, T. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3199750 · Cited by 44
“Polytechnical University (NWPU) [58] datasets are tested in this part.”
Background
Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features
Zhang, J., Li, T., Lu, X., & Cheng, Z. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2536943 · Cited by 44
“It has attracted such intensive attention in visual categorization that the model is introduced into many fields due to its good performance and flexibility, such as scene classification [7], [8], object detection [9], and cloud extraction [10].”
Background
Human Motion Tracking by Multiple RGBD Cameras
Liu, Z., Huang, J., Han, J., Bu, S., & Lv, J. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2564878 · Cited by 44
“is used to transform a pose estimation problem [4] into a per-pixel classification problem [35], [36].”
MethodSelf-citation
RETRACTED: Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images
Zhu, R., Li, Y., Mo, N., & Liu, Y. (2019) · Remote Sensing · doi:10.3390/rs11171996 · Cited by 45
Retracted
Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images
Yang, F., Li, W., Hu, H., Li, W., & Wang, P. (2020) · Sensors · doi:10.3390/s20061686 · Cited by 44
“This simple presentation with an efﬁcient multi-scale network achieve the competent performance on two benchmarks DOTA [35] and NWPU VHR-10 [36].”
Background
Object Detection Based on Global-Local Saliency Constraint in Aerial Images
Li, C., Luo, B., Hong, H., Su, X., Wang, Y., Liu, J., Wang, C., Zhang, J., & Wei, L. (2020) · Remote Sensing · doi:10.3390/rs12091435 · Cited by 46
“…with an iterative training method to solve the problem of detecting inshore ships in high-resolution remote sensing imagery; and Cheng et al. [45] proposed a framework composed of linear SVM, called the collection of part detectors (COPD), to complete the task of multiple category object…”
A Lightweight Object Detection Framework for Remote Sensing Images
Huyan, L., Bai, Y., Li, Y., Jiang, D., Zhang, Y., Zhou, Q., Wei, J., Liu, J., Zhang, Y., & Cui, T. (2021) · Remote Sensing · doi:10.3390/rs13040683 · Cited by 44
“Through experiments on NWPU-VHR-10 [15] and DIOR [16] remote sensing image object detection dataset we show that our framework achieves acceptable performance with fewer parameters and less computational cost compared with state-of-the-art methods [12–14].”
Method
Review of CNN in aerial image processing
Liu, X., Ghazali, K. H., Han, F., & Mohamed, I. I. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2174651 · Cited by 47
SAM-RSIS: Progressively Adapting SAM With Box Prompting to Remote Sensing Image Instance Segmentation
Luo, M., Zhang, T., Wei, S., & Ji, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3460085 · Cited by 53
“3) NWPU VHR-10 Dataset: The NWPU VHR-10 dataset is a multiclass remote sensing object detection dataset [63].”
BackgroundMethod
Incorporating Multiscale Context and Task-Consistent Focal Loss into Oriented Object Detection
Qian, X., Jian, Q., Wang, W., Yao, X., & Cheng, G. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3580937 · Cited by 43
Self-citation
Spectral–spatial feature extraction using orthogonal linear discriminant analysis for classification of hyperspectral data
Shahdoosti, H. R., & Mirzapour, F. (2017) · European Journal of Remote Sensing · doi:10.1080/22797254.2017.1279821 · Cited by 43
“( 4) collection of part detectors which uses a set of part detectors to detect objects or spatial patterns within a certain range of orientation (Cheng, Han, Zhou, & Guo, 2014).”
Background
MM-RCNN: Toward Few-Shot Object Detection in Remote Sensing Images With Meta Memory
Li, J., Tian, Y. W., Xu, Y., Hu, X., Zhang, Z., Hu, W., & Xiao, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3228612 · Cited by 44
“NWPU VHR-10 [63] is a widely used remote sensing object detection benchmark containing 800 images and 2934 manually annotated instances over ten classes: airplane (APL), baseball-diamond (BD), basketball-court (BC), bridge (BR), ground-track-ﬁeld (GTF), harbor (HA), ship (SH), storage-tank (ST),…”
Background
Generalization-Enhanced Few-Shot Object Detection in Remote Sensing
Lin, H., Li, N., Yao, P., Dong, K., Guo, Y., Hong, D., Zhang, Y., & Wen, C. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3528262 · Cited by 48
“…class, making it a valuable resource for evaluating deep learning models in geospatial object detection. b) NWPU VHR-10: The NWPU VHR-10 dataset [70], [71], [72] is a geospatial object detection dataset composed of 715 high-resolution color images collected from Google Earth and 85…”
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
Soni, S., Dudhane, A. A., Debary, H., Fiaz, M., Munir, M. A., Danish, M. S., Fraccaro, P., Watson, C., Klein, L. J., Khan, F. S., et al. (2025) · 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/cvpr52734.2025.01334 · Cited by 79
Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images
Feng, Y., Diao, W., Sun, X., Yan, M., & Gao, X. (2019) · Remote Sensing · doi:10.3390/rs11161901 · Cited by 42
“This operation is very common in high-resolution aerial images like DOTA [43] and NWPUVHR-10 [44] to eliminate the mismatching between RRoIs and corresponding objects for classification and regression.”
Background
A Lightweight Adaptive RoI Extraction Network for Precise Aerial Image Instance Segmentation
Zeng, X., Wei, S., Shi, J., & Zhang, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3121485 · Cited by 43
LHNet: Laplacian Convolutional Block for Remote Sensing Image Scene Classification
Zhang, W., Jiao, L., Liu, F., Liu, J., & Cui, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3192321 · Cited by 41
On-orbit monitoring flying aircraft day and night based on SDGSAT-1 thermal infrared dataset
Li, L., Zhou, X., Hu, Z., Gao, L., Li, X., Ni, X., & Chen, F. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113840 · Cited by 43
MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection
Wu, X., Hong, D., Ghamisi, P., Li, W., & Tao, R. (2018) · Remote Sensing · doi:10.3390/rs10121990 · Cited by 42
“• The collection of part detector (COPD) [41] is composed of a set of representative and discriminative linear support vector machine (SVM) classifier part detectors.”
Background
A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection
Zhuang, S., Wang, P., Jiang, B., Wang, G., & Wang, C. (2019) · Remote Sensing · doi:10.3390/rs11050594 · Cited by 41
“COPD and RICNN are all rotation-invariant frameworks with an SVM classifier for geospatial object detection.”
Influential
Foreground Refinement Network for Rotated Object Detection in Remote Sensing Images
Zhang, T., Zhang, X., Zhu, P., Chen, P., Tang, X., Li, C., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109145 · Cited by 40
“Most earlier works [25], [26] employ handcrafted”
Fine-Grained Ship Detection in High-Resolution Satellite Images With Shape-Aware Feature Learning
Guo, B., Zhang, R., Guo, H., Yang, W. T., Yu, H., Zhang, P., & Zou, T. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3241969 · Cited by 42
“As we can see from Table I, the ﬁrst public dataset for ship detection NWPU [16] was released in 2014, and its size is very small.”
BackgroundMethodInfluential
A Small Object Detection Method for Drone-Captured Images Based on Improved YOLOv7
Zhao, D., Shao, F., Liu, Q., Yang, L., Zhang, H., & Zhang, Z. (2024) · Remote Sensing · doi:10.3390/rs16061002 · Cited by 41
A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery
Xu, F., Liu, J., Sun, M., Zeng, D., & Wang, X. (2017) · Remote Sensing · doi:10.3390/rs9030280 · Cited by 40
Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network
Han, W., Fan, R., Wang, L., Feng, R., Li, F., Deng, Z., & Chen, X. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3038803 · Cited by 39
“There are three data sets used in this study: one small-scale data set, i.e., the Northwest-ern Polytechnical University Very High Resolution 10-Class (NWPU VHR-10) data set [21], and two large-scale data sets, i.e., HRRSD [6] and object DIOR sensing [22].”
MethodInfluential
Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests
Yu, Y., Guan, H., Zai, D., & Ji, Z. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.04.014 · Cited by 39
An Optimized Deep Neural Network Detecting Small and Narrow Rectangular Objects in Google Earth Images
Jiang, S., Yao, W., Wong, M. S., Li, G., Hong, Z., Kuc, T., & Tong, X. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2975606 · Cited by 39
“Therefore, the accuracy distribution shown in Table I is slightly lower than that in [34].”
Result
Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images
Wang, Q., Shen, F., Cheng, L., Jiang, J., He, G., Sheng, W., Jing, N., & Mao, Z. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1811422 · Cited by 39
GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer
Zhang, Y., Liu, X., Wa, S., Chen, S., & Ma, Q. (2022) · Remote Sensing · doi:10.3390/rs14040923 · Cited by 40
Remote sensing image instance segmentation network with transformer and multi-scale feature representation
Ye, W., Zhang, W., Lei, W., Zhang, W., Chen, X., & Wang, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121007 · Cited by 40
A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images
Sun, Z., Meng, C., Cheng, J., Zhang, A., & Chang, S. (2022) · Remote Sensing · doi:10.3390/rs14246312 · Cited by 41
Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models
Chaib, S., Yao, H., Gu, Y., & Amrani, M. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2281755 · Cited by 37
Diverse sample generation with multi-branch conditional generative adversarial network for remote sensing objects detection
Zhu, D., Xia, S., Zhao, J., Zhou, Y. Z., Jian, M., Niu, Q., Yao, R., & Chen, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.065 · Cited by 37
Subject-specific mental workload classification using EEG and stochastic configuration network (SCN)
Pang, L., Guo, L., Zhang, J., Wanyan, X., Qu, H., & Wang, X. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102711 · Cited by 37
Self-citation
Local patchwise minimal and maximal values prior for single optical remote sensing image dehazing
Han, J., Zhang, S., Fan, N., & Ye, Z. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.033 · Cited by 37
Self-citation
WSODet: A Weakly Supervised Oriented Detector for Aerial Object Detection
Tan, Z., Jiang, Z., Guo, C., & Zhang, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3247578 · Cited by 36
“v2 [49] is a ten-class geospatial object detection dataset containing totally 1172 images with the size of 400 × 400 pixels and 2775 instances.”
Background
Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images
Wang, H., Li, H., Qian, W., Diao, W., Zhao, L., Zhang, J., & Zhang, D. (2021) · Remote Sensing · doi:10.3390/rs13081461 · Cited by 39
Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model
Qiu, S., Wen, G., & Fan, Y. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2655098 · Cited by 37
“tions, such as bag-of-visual-words (BoVW) [16] and part-based model [1], [2], based on modern classifiers (support vector machine (SVM) [16]–[18], random forest [19]) and features (SIFT [20] and HOG [21], [22]).”
BackgroundMethod
A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning
Bai, C., Bai, X., & Wu, K. (2023) · Electronics · doi:10.3390/electronics12244902 · Cited by 39
Low-shot learning and class imbalance: a survey
Billion-Polak, P., Prusa, J. D., & Khoshgoftaar, T. M. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00851-z · Cited by 43
“In experiments on two remote-sensing datasets, NWPU VHR-10 [87] and DIOR [88] ( ρ not applicable), their proposed model outperformed all five tested baselines, and an ablation study showed that all model components contributed to the final performance, though the BFS component only contributed 1–3…”
Method
Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature
Cheng, G., Zhou, P., Yao, X., Yao, C., Zhang, Y., & Han, J. (2016) · International Workshop on Earth Observation and Remote Sensing Applications · doi:10.1109/eorsa.2016.7552845 · Cited by 35
“In these comparison methods, Fisher discrimination dictionary learning (FDDL)-based feature [9], traditional HOG feature [24], and HOG feature-based collection of part detectors [4] are employed respectively.”
MethodInfluentialSelf-citation
Applied method for water-body segmentation based on mask R-CNN
Yang, F., Feng, T., Xu, G., & Chen, Y. (2020) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.14.014502 · Cited by 38
Land use classification of high resolution remote sensing images using an encoder based modified GAN architecture
Ansith, S., & Bini, A. A. (2022) · Displays · doi:10.1016/j.displa.2022.102229 · Cited by 35
DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection
Shin, Y., Shin, H.-S., Ok, J.-W., Back, M., Youn, J.-H., & Kim, S. (2024) · Remote Sensing · doi:10.3390/rs16061071 · Cited by 37
SAM-PARSER: Fine-Tuning SAM Efficiently by Parameter Space Reconstruction
Peng, Z., Xu, Z., Zeng, Z., Yang, X., & Wei, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28250 · Cited by 37
An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification
Chaib, S., Gu, Y., & Yao, H. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2501383 · Cited by 34
“More recently, Gong and Han have proposed two novel scene classification and image representation approaches termed multiclass geospatial object detection and geographic image classification based on the collection of part detectors (COPD) [8] and efficient midlevel visual-element-oriented land-use classification usin…”
BackgroundMethod
Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model
Pustokhina, I. V., Pustokhin, D. A., Pareek, P. K., Gupta, D. K., Khanna, A. K., & Shankar, K. (2021) · International Journal of Communication Systems · doi:10.1002/dac.4786 · Cited by 34
“Following that, Cheng et al. 9 developed a rotation invariant using collections of part detectors (COPD), which is applied in the transparent portions of an image.”
Background
Cost-efficient information extraction from massive remote sensing data: When weakly supervised deep learning meets remote sensing big data
Li, Y., Li, X., Zhang, Y., Peng, D., & Bruzzone, L. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103345 · Cited by 34
A sparse representation-based method for infrared dim target detection under sea–sky background
Wang, X., Shen, S., Ning, C., Xu, M., & Yan, X. (2015) · Infrared Physics & Technology · doi:10.1016/j.infrared.2015.05.014 · Cited by 34
Few-Shot Object Detection With Self-Adaptive Global Similarity and Two-Way Foreground Stimulator in Remote Sensing Images
Zhang, Y., Zhang, B., & Wang, B. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3203126 · Cited by 34
“W ITHtherapiddevelopmentofcomputervision,moreand more methods dealing with object detection in remote sensing images (RSIs) have emerged [1], [2], [3], [4], [5], [6], [7], [8], [9].”
BackgroundMethod
GLFE-YOLOX: Global and Local Feature Enhanced YOLOX for Remote Sensing Images
Gu, Q., Huang, H., Han, Z., Fan, Q., & Li, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3387499 · Cited by 34
“2) NWPU VHR-10 [39]: NWPU VHR-10 is a high-resolution geographic remote sensing dataset.”
Background
Rotation-Invariant Object Detection in High-Resolution Satellite Imagery Using Superpixel-Based Deep Hough Forests
Yu, Y., Guan, H., & Ji, Z. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2432135 · Cited by 33
“A collection of part detectors was used in [13] for detecting multiclass geospatial objects.”
Method
Enhanced Feature Representation in Detection for Optical Remote Sensing Images
Fu, K., Chen, Z., Zhang, Y., & Sun, X. (2019) · Remote Sensing · doi:10.3390/rs11182095 · Cited by 33
“Compared with other public optical remote image datasets, including UCAS-AOD [23], NWPU VHR-10 [24] and VEDAI [25], DOTA covers the most categories and the largest quantity scale.”
Method
A Review of Remote Sensing Image Object Detection Algorithms Based on Deep Learning
Zheng, Z., Lei, L., Sun, H., & Kuang, G. (2020) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc50857.2020.9177453 · Cited by 35
“It ’ s essence is to learn complex features in the data by building a convolutional neural network with multi-layer structure and a large number of training data [2], which is used for later classification, detection and other applications.”
Background
Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images
Han, W., Kuerban, A., Yang, Y., Huang, Z., Liu, B., & Gao, J. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3044422 · Cited by 33
A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN
Song, Z., Sui, H., & Li, H. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1826059 · Cited by 33
“A wealth of investigation has been proposed during the past two decades to solve the problems encountered in airplane detection (Cheng et al. 2014; Yu, Guan, and Zai 2016; Zhu et al. 2019) and ship detection (Cheng et al. 2014; Tang et al. 2014; Li et al. 2016; Yang et al. 2018; Li et al. 2018) of…”
BackgroundInfluential
Structured Object-Level Relational Reasoning CNN-Based Target Detection Algorithm in a Remote Sensing Image
Cheng, B., Li, Z., Xu, B., Xu, Y. H., Ding, Z., & Qin, T. (2021) · Remote Sensing · doi:10.3390/rs13020281 · Cited by 33
Solo-to-Collaborative Dual-Attention Network for One-Shot Object Detection in Remote Sensing Images
Li, L., Yao, X., Cheng, G., Xu, M., Han, J., & Han, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3091003 · Cited by 33
“v2 is released by Northwestern Polytechnical University in 2014 [53].”
MethodSelf-citation
RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery
Zhu, Y., Sun, X., Diao, W., Li, H., & Fu, K. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3190699 · Cited by 36
FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite ImagesPreprint
Chen, K., Wu, M., Liu, J., & Zhang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06832 · Cited by 33
“There are also some ship detection dataset based on highresolution remote sensing optical images, such as DOTA [1] ,NWPU VHR-10 [2], but most of these dataset group all ships into one category.”
Background
Adaptive Saliency Biased Loss for Object Detection in Aerial Images
Sun, P., Chen, G., & Shang, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2980023 · Cited by 33
A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series
Lin, X., Wa, S., Zhang, Y., & Ma, Q. (2022) · Remote Sensing · doi:10.3390/rs14081771 · Cited by 32
“This dataset is an open-source level 10 geospatial remote sensing dataset, containing 800 images in total—650 object images and 150 background images, with ten categories of objects: aircraft, vehicles, ships, ports, baseball fields, athletic fields, bridges, basketball courts, tennis fields, and oil tanks [28].”
Background
Deep learning in different remote sensing image categories and applications: status and prospects
Bai, Y., Zhao, Y., Shao, Y., Zhang, X., & Yuan, X. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2048319 · Cited by 34
“…for scene classification, object detection, and image segmentation are composed of aerial images, such as the UCMerced_LandUse dataset (Yang and Newsam 2010), NWPU VHR-10 (Cheng et al. 2014), Aerial Image Segmentation Dataset (Kaiser et al. 2017), and WHU Building Dataset (Ji, Wei and Lu 2019).”
MethodInfluential
Trade-off and synergistic of ecosystem services supply and demand based on socio-ecological system (SES) in typical hilly regions of south China
You, C., Qu, H., Wang, C., Feng, C., & Guo, L. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.111749 · Cited by 31
Self-citation
A multi-scale semantically enriched feature pyramid network with enhanced focal loss for small-object detection
Kiobya, T., Zhou, J., & Maiseli, B. J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113003 · Cited by 31
Structural Alignment based Zero-shot Classification for Remote Sensing Scenes
Quan, J., Wu, C., Wang, H., & Wang, Z. (2018) · IEEE International Conference on Electronics and Communication Engineering (ICECE) · doi:10.1109/icecome.2018.8645056 · Cited by 31
“INTRODUCTION With the rapid development of satellite remote sensing technology, remote sensing scene classification has attracted widespread attention [1,2].”
Background
A novel application of deep learning with image cropping: a smart city use case for flood monitoring
Mishra, B. K., Thakker, D., Mazumdar, S., Neagu, D. C., Gheorghe, M., & Simpson, S. (2020) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-020-00099-x · Cited by 32
“Multi-object detection approaches had been applied to classify images which constitute multiple objects representing multiple scenarios [30, 31].”
Method
An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images
Guo, Y., Tong, X., Xu, X., Liu, S., Feng, Y., & Xie, H. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3207178 · Cited by 31
“Our DA 2 FNet was evaluated on NWPU VHR-10 [8], RSOD [9], and DOTA [10] Compared with DMNet [11] and CDMNet [12] exploiting DMs to crape images, our work takes the whole image as input for the DM estimation and target detection.”
Method
Geospatial Object Detection via Deconvolutional Region Proposal Network
Wang, C., Shi, J., Yang, X., Zhou, Y., Wei, S., Li, L., & Zhang, X. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2919382 · Cited by 30
“1) Dataset Description: The experiments use the NWPU VHR-10 dataset for network training and testing [35], in which ten-class objects are airplanes, ships, storage tanks, baseball diamonds, tennis courts, basketball courts, ground track fields, harbors, bridges, and vehicles.”
Method
Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images
Fan, F., Zeng, X., Wei, S., Zhang, H., Tang, D., Shi, J., & Zhang, X. (2022) · Remote Sensing · doi:10.3390/rs14030531 · Cited by 30
“Following the original division ratio of 7:3 in [15] that is used for generating the training set and test set, we obtain the training set and test set for our experiments.”
Method
Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector
Zhang, H., Zhang, X., Meng, G., Guo, C., & Jiang, Z. (2022) · Remote Sensing · doi:10.3390/rs14122790 · Cited by 32
“NWPU VHR-10 10 Airplane, storage tanks, ship, baseball fields, ground runways, etc. 1 Ship 302 DOTA 15 Airplane, bridge, swimming pool,ship, roundabout, etc. 1 Ship 6300 LEVIR 3 Airplane, ship, oilpot 1 Ship 3025 HRSC2016 19 Medical boat, Ticonderoga,Nimitz, Perry, Austin, etc. 19 Medical boat, Ticonderoga, Nimitz, Pe…”
Background
Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron
Minu, M. S., & Canessane, R. A. (2022) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2022.104652 · Cited by 30
Multigranularity Self-Attention Network for Fine-Grained Ship Detection in Remote Sensing Images
Ouyang, L., Fang, L., & Ji, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3220503 · Cited by 32
“Among the remote sensing detection datasets [29], [30], [31], [40], [41], [42], [43], [44], [45], the fine-grained ship detection datasets are not sufficient and only three related datasets have been established [29], [30], [31].”
Background
A deep neural network for small object detection in complex environments with unmanned aerial vehicle imagery
Jobaer, S., Tang, X., & Zhang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110466 · Cited by 31
ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images
Ye, X., Xiong, F., Lu, J., Zhou, J. W., & Qian, Y. (2020) · Remote Sensing · doi:10.3390/rs12244027 · Cited by 29
“, bag-of-words (BoW) feature, were commonly explored for detecting RS objects [10].”
BackgroundMethodInfluential
Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint
Xie, H., Chen, Y., & Ghamisi, P. (2021) · Remote Sensing · doi:10.3390/rs13132566 · Cited by 32
“At present, RS images have been used in many research domains, including object detection [1–3], image retrieval [4,5], change detection [6,7], land use classiﬁcation [8] and environmental monitoring [9].”
Method
Double Head Predictor based Few-Shot Object Detection for Aerial Imagery
Wolf, S., Meier, J., Sommer, L. W., & Beyerer, J. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00086 · Cited by 29
“For comparison with state-of-the-art approaches, we evaluate our method on the NWPU VHR-10 [6] dataset.”
Method
AFDet: Toward More Accurate and Faster Object Detection in Remote Sensing Images
Liu, N., Çelik, T., Zhao, T., Zhang, C., & Li, H.-C. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3128566 · Cited by 29
Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification
Xu, F., Liu, J., Dong, C., & Wang, X. (2017) · Remote Sensing · doi:10.3390/rs9100985 · Cited by 29
Retentive Compensation and Personality Filtering for Few-Shot Remote Sensing Object Detection
Wu, J., Lang, C., Cheng, G., Xie, X., & Han, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3367168 · Cited by 28
“…image datasets [1], [2], [3], the field of remote sensing image interpretation based on deep learning possesses extensive research, especially for object detection [4], [5], image classification [2], [6], object recognition [7], [8], image segmentation [9], [10], and object tracking tasks [11].”
BackgroundMethodSelf-citation
GDRS-YOLO: More Efficient Multiscale Features Fusion Object Detector for Remote Sensing Images
Lin, Y., Li, J., Shen, S., Wang, H., & Zhou, H. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3397717 · Cited by 28
“We use the NWPU VHR-10 [16] and VisDrone [17] datasets for experiments.”
Method
Hierarchical fusion and divergent activation based weakly supervised learning for object detection from remote sensing images
Wu, Z., Xu, J., Wang, Y., Sun, F., Tan, M., & Weise, T. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.10.010 · Cited by 27
FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection
Xu, W., Zhang, C., Wang, Q., & Dai, P. (2022) · Sensors · doi:10.3390/s22186993 · Cited by 28
“NWPU VHR-10 [45] is a classic high-deﬁnition aerial object detection remote sensing dataset.”
BackgroundMethod
Object detection in high resolution optical image based on deep learning technique
Qi, W. (2022) · Natural Hazards Research · doi:10.1016/j.nhres.2022.10.002 · Cited by 28
Complex Optical Remote-Sensing Aircraft Detection Dataset and Benchmark
Shi, T., Gong, J., Jiang, S., Zhi, X., Bao, G., Sun, Y., & Zhāng, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3283137 · Cited by 29
“NWPU VHR-10 [12], constructed by Northwestern Poly-technical University, originates from the Google Earth and Vaihingen dataset, of which 88 images contain 757 aircraft instances.”
A large scale training sample database system for intelligent interpretation of remote sensing imagery
Cao, Z., Jiang, L., Yue, P., Gong, J., Hu, X., Liu, S., Tan, H., Liu, C., Shangguan, B., & Yu, D. (2023) · Geo-spatial Information Science · doi:10.1080/10095020.2023.2244005 · Cited by 28
“For scene classification, datasets such as NWPU-RESISC45 (Cheng, Han, and Lu 2017), RSD46-WHU (Long et al. 2017), RSI-CB (Li et al. 2017), and PatternNet (Zhou et al. 2018) have been created Object detection datasets (Cheng and Han 2016) include NWPU VHR-10 (Cheng et al. 2014), DIOR (Li et al. 2020), DOTA (Ding et al.…”
Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding
Gan, J., Lu, W., Li, Q., Zhang, Z., Yang, J., Ma, Y., & Yao, W. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2669206 · Cited by 28
Toward Open-World Remote Sensing Imagery Interpretation: Past, present, and future
Lang, C., Cheng, G., Wu, J., Li, Z., Xie, X., Li, J., & Han, J. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3505602 · Cited by 30
Self-citation
Mining Deep Semantic Representations for Scene Classification of High-Resolution Remote Sensing Imagery
Hu, F., Xia, G.-S., Yang, W. T., & Zhang, L. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2916880 · Cited by 26
“SPM [6] 74 COPD [65] 91.”
A multiscale object detection approach for remote sensing images based on MSE-DenseNet and the dynamic anchor assignment
Zhu, H., Zhang, P., Wang, L., Zhang, X., & Jiao, L. (2019) · Remote Sensing Letters · doi:10.1080/2150704x.2019.1633486 · Cited by 27
“To evaluate the performance of the proposed method, we trained and validated our method on NWPU VHR-10 dataset, a publicly available 10-class geospatial objectdetection dataset produced by Cheng et al. (2014), who achieved a detection mAP of 72.”
MethodInfluential
Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images
Körez, A., Barışçı, N., Çetin, A., & Ergün, U. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9060370 · Cited by 28
“Cheng et al. [10] developed a rotation-invariant framework based on the Collection of Part Detectors (COPD) for multiclass object detection.”
BackgroundMethodResultInfluential
Contextual Information-Preserved Architecture Learning for Remote-Sensing Scene Classification
Chen, J., Huang, H., Peng, J., Zhu, J., Chen, L., Tao, C., & Li, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3063287 · Cited by 28
“Designed features are frequently used in remote-sensing scene classiﬁcation, such as color histograms and texture descriptors [35], SIFT [10], HOG [4], and bag-of-visual-words [6].”
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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