A light and faster regional convolutional neural network for object detection in optical remote sensing images

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Untitled work

(2014) · Cited by 33,419

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

Deep learning in remote sensing applications: A meta-analysis and review

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

AlMansour, N., Syed, H. F., Khayat, N. R., Al-Theeb, R., Juri, R. E., Alhiyafi, J. A., Alrashed, S., & Olatunji, S. O. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.017 · Cited by 325

Computer vision applications in construction safety assurance

Fang, W., Ding, L., Love, P. E. D., Luo, H., Li, H., Pena-Mora, F. A., Zhong, B., & Zhou, C. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.103013 · Cited by 328

A graph convolutional neural network for classification of building patterns using spatial vector data

Yan, X., Ai, T., Yang, M. J., & Yin, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.010 · Cited by 280

Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

Li, W., He, C., Fang, J., Zheng, J., Fu, H., & Yu, L. (2019) · Remote Sensing · doi:10.3390/rs11040403 · Cited by 251

Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet

Chaturvedi, S. S., Gupta, K., & Prasad, P. S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3383-9_15 · Cited by 238

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., & Gao, Y. (2022) · Remote Sensing · doi:10.3390/rs14102385 · Cited by 230

Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)

Hao, Z., Lin, L., Post, C. J., Mikhailova, E. A., Li, M., Chen, Y., Yu, K., & Liu, J. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.06.003 · Cited by 205

How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

Zhang, X., Han, L., Han, L., & Zhu, L. (2020) · Remote Sensing · doi:10.3390/rs12030417 · Cited by 194

Remote Sensing Object Detection Meets Deep Learning: A metareview of challenges and advances

Zhang, X., Zhang, T., Wang, G., Zhu, P., Tang, X., Jia, X., & Jiao, L. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3312347 · Cited by 219

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Hoeser, T., Bachofer, F., & Kuenzer, C. (2020) · Remote Sensing · doi:10.3390/rs12183053 · Cited by 188

YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

Pham, M., Courtrai, L., Friguet, C., Lefèvre, S., & Baussard, A. (2020) · Remote Sensing · doi:10.3390/rs12152501 · Cited by 175

Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks

Li, W., Dong, R., Fu, H., & Yu, L. (2018) · Remote Sensing · doi:10.3390/rs11010011 · Cited by 164

Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images

Li, Y., Zhang, Y., Huang, X., & Yuille, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.09.014 · Cited by 144

Feature Split–Merge–Enhancement Network for Remote Sensing Object Detection

Ma, W., Li, N., Zhu, H., Jiao, L., Tang, X., Guo, Y., & Hou, B. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3140856 · Cited by 146

A New Spatial-Oriented Object Detection Framework for Remote Sensing Images

Yu, D., & Ji, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3127232 · Cited by 140

Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery

Zhang, W., Witharana, C., Liljedahl, A., & Kanevskiy, M. (2018) · Remote Sensing · doi:10.3390/rs10091487 · Cited by 133

Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

Du, Z., Yang, J., Ou, C., & Zhang, T. (2019) · Remote Sensing · doi:10.3390/rs11070888 · Cited by 120

Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

Ma, X., Ji, K., Xiong, B., Zhang, L., Feng, S., & Kuang, G. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3120009 · Cited by 112

Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

Zhou, L., Wei, H., Li, H., Zhao, W., Zhang, Y., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3041025 · Cited by 133

Combining deep learning and ontology reasoning for remote sensing image semantic segmentation

Li, Y., Ouyang, S., & Zhang, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108469 · Cited by 104

Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function

Shivappriya, S. N., Priyadarsini, M. J. P., Stateczny, A., Puttamadappa, C., & Parameshachari, B. D. (2021) · Remote Sensing · doi:10.3390/rs13020200 · Cited by 94

HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery

Zheng, Z., Zhong, Y., Ma, A., Han, X., Zhao, J. J., Liu, Y., & Zhang, L. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.04.019 · Cited by 91

YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images

Wan, D., Lu, R., Wang, S., Shen, S., Xu, T., & Lang, X. (2023) · Remote Sensing · doi:10.3390/rs15030614 · Cited by 99

Detecting and mapping tree seedlings in UAV imagery using convolutional neural networks and field-verified data

Pearse, G. D., Tan, Y. S., Watt, M. S., Franz, M., & Dash, J. P. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.08.005 · Cited by 88

Convolutional neural network approach for automatic tympanic membrane detection and classification

Başaran, E., Cömert, Z., & Çelik, Y. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101734 · Cited by 86

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

Huang, Z., Li, W., Xia, X., Wang, H., Jie, F., & Tao, R. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3067470 · Cited by 86

Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery

Zhang, T., Zhuang, Y., Wang, G., Dong, S., Chen, H., & Li, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3108476 · Cited by 84

Geographical Knowledge-Driven Representation Learning for Remote Sensing Images

Li, W., Chen, K., Chen, H., & Shi, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3115569 · Cited by 93

Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme

Ajayi, O. G., & Ashi, J. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100128 · Cited by 92

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Nawaz, S. A., Li, J., Bhatti, U. A., Shoukat, M. U., & Ahmad, R. M. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1041514 · Cited by 90

A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images

Qiu, H., Li, H., Wu, Q., Meng, F., Ngan, K. N., & Shi, H. (2019) · Remote Sensing · doi:10.3390/rs11131594 · Cited by 77

Deep learning-based automatic detection of productive tillers in rice

Deng, R., Jiang, Y., Tao, M., Huang, X., Bangura, K., Liu, C., Lin, J., & Qi, L. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105703 · Cited by 72

A systematic review of object detection from images using deep learning

Kaur, J., & Singh, W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15981-y · Cited by 78

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

Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform

Zhang, J., Jia, X., Hu, J., & Tan, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3066696 · Cited by 70

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

Saeedimoghaddam, M., & Stepinski, T. F. (2019) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2019.1696968 · Cited by 71

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

Verschoof-van der Vaart, W. B., Lambers, K., Kowalczyk, W., & Bourgeois, Q. P. J. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9050293 · Cited by 67

Transferable deep learning model based on the phenological matching principle for mapping crop extent

Ge, S., Zhang, J., Pan, Y., Yang, Z., & Zhu, S. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102451 · Cited by 70

X-LineNet: Detecting Aircraft in Remote Sensing Images by a Pair of Intersecting Line Segments

Wei, H., Zhang, Y., Wang, B., Yang, Y., Li, H., & Wang, H. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2999082 · Cited by 63

Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset

Olatunji, S. O., Alotaibi, S., Almutairi, E., Alrabae, Z., Almajid, Y., Altabee, R., Altassan, M., Ahmed, M. I. B., Farooqui, M., & Alhiyafi, J. A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104267 · Cited by 63

Learning to Classify Structures in ALS-Derived Visualizations of Ancient Maya Settlements with CNN

Somrak, M., Džeroski, S., & Kokalj, Ž. (2020) · Remote Sensing · doi:10.3390/rs12142215 · Cited by 65

Vehicle Detection From High-Resolution Remote Sensing Imagery Using Convolutional Capsule Networks

Yu, Y., Gu, T., Guan, H., Li, D., & Jin, S. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2912582 · Cited by 56

Coupling of deep learning and remote sensing: a comprehensive systematic literature review

Yasir, M., Jianhua, W., Liu, S., Sheng, H., Xu, M., & Hossain, M. S. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2161856 · Cited by 50

Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks

Du, L., McCarty, G. W., Zhang, X., Lang, M., Vanderhoof, M. K., Li, X., Huang, C., Lee, S., & Zou, Z. (2020) · Remote Sensing · doi:10.3390/rs12040644 · Cited by 50

A fast self-attention cascaded network for object detection in large scene remote sensing images

Hua, X., Wang, X., Rui, T., Zhang, H., & Wang, D. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106495 · Cited by 48

A 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

Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-Based Wake Detection via Optical Images

Xue, F., Jin, W., Qiu, S., & Yang, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3128989 · Cited by 46

Aircraft detection in satellite imagery using deep learning-based object detectors

Azam, B., Khan, M. J., Bhatti, F. A., Maud, A. R., Hussain, S. F., Hashmi, A. J., & Khurshid, K. (2022) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2022.104630 · Cited by 49

Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

Liu, W., Wang, J., Luo, J., Wu, Z., Chen, J., Zhou, Y., Sun, Y., Shen, Z., Xu, N., & Yang, Y. (2020) · Remote Sensing · doi:10.3390/rs12223733 · Cited by 48

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Song, G., Wu, S., Lee, C. K. F., Serbin, S. P., Wolfe, B. T., Ng, M. K., Ely, K., Bogonovich, M., Wang, J., Lin, Z., et al. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.10.023 · Cited by 46

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

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

An Approach on Image Processing of Deep Learning Based on Improved SSD

Jin, L., & Liu, G. (2021) · Symmetry · doi:10.3390/sym13030495 · Cited by 42

Online Structured Sparsity-Based Moving-Object Detection From Satellite Videos

Zhang, J., Jia, X., Hu, J., & Chanussot, J. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2976855 · Cited by 40

CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

Lin, Q., Zhao, J., Fu, G., & Yuan, Z. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027924 · Cited by 40

Tree-based convolutional neural networks for object classification in segmented satellite images

Robinson, H., Vimal, S., Khari, M., López, F., & Crespo, R. A. G. (2020) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342020945026 · Cited by 39

Vision-Based Activity Classification of Excavators by Bidirectional LSTM

Kim, I.-S., Latif, K., Kim, J., Sharafat, A., Lee, D., & Seo, J. (2022) · Applied Sciences · doi:10.3390/app13010272 · 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

Orientation guided anchoring for geospatial object detection from remote sensing imagery

Yu, Y., Guan, H., Li, D., Gu, T., Tang, E., & Li, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.12.001 · Cited by 36

Few-shot incremental learning with continual prototype calibration for remote sensing image fine-grained classification

Zhu, Z., Wang, P., Diao, W., Yang, J., Wang, H., & Sun, X. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.024 · Cited by 33

A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

Cira, C.-I., Alcarria, R., Manso-Callejo, M.-Á., & Serradilla, F. (2020) · Remote Sensing · doi:10.3390/rs12050765 · Cited by 20

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Zeng, F., Cheng, L., Li, N., Xia, N., Ma, L., Zhou, X., & Li, M. (2019) · Remote Sensing · doi:10.3390/rs11192204 · Cited by 33

Objects detection for remote sensing images based on polar coordinatesPreprint

Zhou, L., Wei, H., Li, H., Zhao, W., Zhang, Y., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02988 · 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

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

Instance Segmentation in Remote Sensing Imagery using Deep Convolutional Neural Networks

Potlapally, A., Chowdary, P. S. R., Shekhar, S. S. R., Mishra, N., Madhuri, C. S. V. D., & Prasad, A. V. V. (2019) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i46837.2019.9055569 · Cited by 28

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

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

Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks

Liu, W., Cheng, D., Yin, P., Yang, M., Li, E., Xie, M., & Zhang, L. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8010049 · Cited by 27

An Object-Based Approach for Mapping Crop Coverage Using Multiscale Weighted and Machine Learning Methods

Tang, Z., Wang, H., Li, X., Li, X., Cai, W., & Han, C. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2983439 · Cited by 27

POI Detection of High-Rise Buildings Using Remote Sensing Images: A Semantic Segmentation Method Based on Multitask Attention Res-U-Net

Li, B., Gao, J., Chen, S., Lim, S., & Jiang, H. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3174399 · Cited by 25

Potential of eye-tracking for interactive geovisual exploration aided by machine learning

Keskin, M., & Kettunen, P. (2023) · International Journal of Cartography · doi:10.1080/23729333.2022.2150379 · Cited by 25

SOCDet: A Lightweight and Accurate Oriented Object Detection Network for Satellite On-Orbit Computing

Pang, Y., Zhang, Y., Kong, Q., Wang, Y., Chen, B., & Cao, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3269642 · Cited by 24

Analyzing CARLA ’s performance for 2D object detection and monocular depth estimation based on deep learning approaches

Tabata, A. N., Zimmer, A., Coelho, L. D. S., & Mariani, V. C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120200 · Cited by 24

A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

Xie, W., Qin, H., Li, Y., Wang, Z., & Lei, J. (2019) · Remote Sensing · doi:10.3390/rs11111376 · Cited by 23

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

Tien, P. W., Wei, S., Calautit, J. K., Darkwa, J., & Wood, C. J. (2020) · Journal of Sustainable Development of Energy Water and Environment Systems · doi:10.13044/j.sdewes.d8.0378 · Cited by 22

Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

Mo, N., Li, Y., Zhu, R., & Xie, H. T. (2019) · Remote Sensing · doi:10.3390/rs11030272 · Cited by 22

Ship Detection for Optical Remote Sensing Images Based on Visual Attention Enhanced Network

Bi, F., Hou, J., Chen, L., Yang, Z., & Wang, Y. (2019) · Sensors · doi:10.3390/s19102271 · Cited by 23

Development and evolution of YOLO in object detection: A survey

Tian, Y., Xu, W., Yang, B., Yang, X., Guo, H., Wang, G., & Yu, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132436 · Cited by 33

A novel method for vehicle detection in high-resolution aerial remote sensing images using YOLT approach

Lavanya, K., V, Karnick, S., Ghalib, M. R., Shankar, A., Khapre, S. P., & Tayubi, I. A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12613-9 · Cited by 25

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Liu, Y., Su, J., Shen, L., Lu, N., Fang, Y., Liu, F., Song, Y., & Su, B. (2021) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20211405.6593 · Cited by 23

Point Cloud Semantic Segmentation Network Based on Multi-Scale Feature Fusion

Du, J., Jiang, Z., Huang, S., Wang, Z., Su, J., Su, S., Wu, Y., & Cai, G. (2021) · Sensors · doi:10.3390/s21051625 · Cited by 21

Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures

Callejo, M. Á. M., Cira, C.-I., Alcarria, R., & Justel, J. J. A. (2020) · Remote Sensing · doi:10.3390/rs12223743 · Cited by 20

Parcel-Level Mapping of Horticultural Crop Orchards in Complex Mountain Areas Using VHR and Time-Series Images

Jiao, S., Hu, D., Shen, Z., Wang, H., Dong, W., Guo, Y., Li, S., Lei, Y., Kou, W., Wang, J., et al. (2022) · Remote Sensing · doi:10.3390/rs14092015 · Cited by 22

A Specially Optimized One-Stage Network for Object Detection in Remote Sensing Images

Qin, H., Li, Y., Lei, J., Xie, W., & Wang, Z. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.2975086 · Cited by 17

A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

Kouvaras, L., & Petropoulos, G. P. (2024) · Drones · doi:10.3390/drones8020043 · Cited by 17

Geographical Supervision Correction for Remote Sensing Representation Learning

Li, W., Chen, K., & Shi, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3202499 · Cited by 19

MCCANet: A multispectral class-constraint attentional neural network for object detection in mining scenes

Wu, Z., Li, H., Wang, Y., & Long, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123233 · Cited by 16

TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning

Singh, L., Janghel, R. R., & Sahu, S. P. (2020) · Data Technologies and Applications · doi:10.1108/dta-06-2020-0126 · Cited by 17

First Dataset of Wind Turbine Data Created at National Level With Deep Learning Techniques From Aerial Orthophotographs With a Spatial Resolution of 0.5 M/Pixel

Callejo, M. Á. M., Cira, C.-I., Alcarria, R., & Matesanz, F. J. G. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3101934 · Cited by 16

U-Net feature fusion for multi-class semantic segmentation of urban fabrics from Sentinel-2 imagery: an application on Grand Est Region, France

Wenger, R., Puissant, A., Weber, J., Idoumghar, L., & Forestier, G. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2054295 · Cited by 15

A review on deep learning-based object tracking methods

Uke, N., Futane, P. R., Deshpande, N., & Uke, S. N. (2024) · Multiagent and Grid Systems · doi:10.3233/mgs-230126 · Cited by 15

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

Cao, S., Yu, Y., Guan, H., Peng, D., & Yan, W. (2019) · Remote Sensing · doi:10.3390/rs11141708 · Cited by 15

Appearance and Motion Based Persistent Multiple Object Tracking in Wide Area Motion Imagery

Sommer, L. W., Krüger, W. A., & Teutsch, M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00434 · Cited by 14

Anchor-free object detection in remote sensing images using a variable receptive field network

Fu, S., He, Y., Du, X., & Zhu, Y. (2023) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-023-01013-2 · Cited by 14

Bibliometric and visualized analysis of deep learning in remote sensing

Bai, Y., Sun, X., Ji, Y., Huang, J., Fu, W., & Shi, H. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1949069 · Cited by 14

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

Multimodal Knowledge Distillation for Arbitrary-Oriented Object Detection in Aerial Images

Huang, Z., Li, W., & Tao, R. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097119 · Cited by 13

Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN

Xiu, C., Zhang, Q., Han, J., Han, X., Liu, Y., & Fang, Y. (2019) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc47050.2019.9064409 · Cited by 16

$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

Pang, J., Li, C., Shi, J., Xu, Z., & Feng, H. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2899955 · Cited by 274

High-Performance Object Detection for Optical Remote Sensing Images with Lightweight Convolutional Neural Networks

Liu, J., Liu, R., Ren, K., Li, X., Xiang, J., & Qiu, S. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00074 · Cited by 10

Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Hua, X., Wang, X., Rui, T., Wang, D., & Shao, F. (2019) · Electronics · doi:10.3390/electronics8101151 · Cited by 11

A multi-scale fine-grained object detection method based on deep CNN in remote sensing images

Xie, Q., Zhou, D., & Liu, Z. (2024) · International Conference on Optics and Machine Vision (ICOMV) · doi:10.1117/12.3031816