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Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Abstract
Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant feature transform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.
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Cites
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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324
Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057
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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Convolutional neural networks for large-scale remote-sensing image classification
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
Joint deep learning for land cover and land use classification
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.014 · Cited by 451
Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey
Wu, X., Li, W., Hong, D., Tao, R., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3115137 · Cited by 421
Learning multiscale and deep representations for classifying remotely sensed imagery
Zhao, W., & Du, S. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.004 · Cited by 398
Deep learning for decentralized parking lot occupancy detection
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.055 · Cited by 397
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
Mundhenk, T. N., Konjevod, G., Sakla, W. A., & Boakye, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_48 · Cited by 386
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., & Ouni, K. (2019) · 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS) · doi:10.1109/uvs.2019.8658300 · Cited by 307
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Surface Water Mapping by Deep Learning
Isikdogan, F., Bovik, A. C., & Passalacqua, P. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2735443 · Cited by 292
Liang, H., & Li, Q. (2016) · Remote Sensing · doi:10.3390/rs8020099 · Cited by 238
Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.191 · Cited by 196
Deng, Z., Sun, H., Zhou, S., Zhao, J., & Zou, H. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2694890 · Cited by 195
Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN
Xu, Y., Yu, G., Wang, Y., Wu, X., & Ma, Y. (2017) · Journal of Advanced Transportation · doi:10.1155/2017/2823617 · Cited by 183
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Liu, L., Pan, Z., & Lei, B. (2017) · arXiv · doi:10.48550/arxiv.1711.09405 · Cited by 166
Fast Deep Vehicle Detection in Aerial Images
Sommer, L. W., Schuchert, T., & Beyerer, J. (2017) · 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2017.41 · Cited by 139
Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network
Zhu, J., Sun, K., Jia, S., Li, Q., Hou, X., Lin, W., Liu, B., & Qiu, G. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2879368 · Cited by 127
Fast vehicle detection in UAV images
Tang, T., Deng, Z., Zhou, S., Lei, L., & Zou, H. (2017) · 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) · doi:10.1109/rsip.2017.7958795 · Cited by 74
VAID: An Aerial Image Dataset for Vehicle Detection and Classification
Lin, H.-Y., Tu, K.-C., & Li, C.-Y. (2020) · IEEE Access · doi:10.1109/access.2020.3040290 · Cited by 70
Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos
Wang, J., Simeonova, S., & Shahbazi, M. (2019) · Remote Sensing · doi:10.3390/rs11182155 · Cited by 50
Deep neural networks-based vehicle detection in satellite images
Jiang, Q., Cao, L., Cheng, M., Wang, C., & Li, J. (2015) · 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB) · doi:10.1109/isbb.2015.7344954 · Cited by 45
Vehicle Detection Based on Drone Images with the Improved Faster R-CNN
Wang, L., Liao, J., & Xu, C. (2019) · Proceedings of the 2019 11th International Conference on Machine Learning and Computing · doi:10.1145/3318299.3318383 · Cited by 31
Truck traffic monitoring with satellite images
Kaack, L. H., Chen, G. H., & Morgan, M. G. (2019) · Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3314344.3332480 · Cited by 24
Automatic object tracking in optimized UAV video
Micheal, A. A., & Vani, K. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02782-0 · Cited by 7
Metadata
- Authors
- Chen, X., Xiang, S., Liu, C.-L., & Pan, C.-H.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2014-10
- Volume
- 11
- Issue
- 10
- Pages
- 1797-1801
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2066916495
- Cited by
- 622
- References
- 7
- FWCI
- 43.62
- Type
- journal article
- Language
- en
- Open access
- closed
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