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On the $ k $-error linear complexity for $ p^n $-periodic binary sequences via hypercube theory
Big Map R-CNN for object detection in large-scale remote sensing images
a. | FIST LAB, School of Information Science and Engineering, Yunnan University Kunming, 650091, Yunnan, China |
b. | Yunnan Union Vision Technology Co Ltd. Kunming, 650091, Yunnan, China |
c. | School of Software, Yunnan University Kunming, Yunnan University Kunming, 650091, Yunnan, China |
Detecting sparse and multi-sized objects in very high resolution (VHR) remote sensing images remains a significant challenge in satellite imagery applications and analytics. Difficulties include broad geographical scene distributions and high pixel counts in each image: a large-scale satellite image contains tens to hundreds of millions of pixels and dozens of complex backgrounds. Furthermore, the scale of the same category object can vary widely (e.g., ships can measure from several to thousands of pixels). To address these issues, here we propose the Big Map R-CNN method to improve object detection in VHR satellite imagery. Big Map R-CNN introduces mean shift clustering for quadric detecting based on the existing Mask R-CNN architecture. Big Map R-CNN considers four main aspects: 1) big map cropping to generate small size sub-images; 2) detecting these sub-images using the typical Mask R-CNN network; 3) screening out fragmented low-confidence targets and collecting uncertain image regions by clustering; 4) quadric detecting to generate prediction boxes. We also introduce a new large-scale and VHR remote sensing imagery dataset containing two categories (RSI LS-VHR-2) for detection performance verification. Comprehensive evaluations on RSI LS-VHR-2 dataset demonstrate the effectiveness of the proposed Big Map R-CNN algorithm for object detection in large-scale remote sensing images.
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3D object recognition with deep belief nets, Advances in Neural Information Processing Systems, (2009), 1339-1347.
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H. Noh, S. Hong and B. Han,
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[29] |
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Deepid-net: Deformable deep convolutional neural networks for object detection, The IEEE Conference on Computer Vision and Pattern Recognition, (2015), 2403-2412.
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[30] |
M. T. Pham, G. Mercier and O. Regniers,
Texture retrieval from VHR optical remote sensed images using the local extrema descriptor with application to vineyard parcel detection, Remote Sensing, 8 (2016), 368-388.
doi: 10.3390/rs8050368. |
[31] |
J. Redmon, S. Divvala and R. Girshick,
You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 779-788.
doi: 10.1109/CVPR.2016.91. |
[32] |
Y. Ren, C. Zhu and S. Xiao,
Small object detection in optical remote sensing images via modified faster R-CNN, Applied Sciences, 8 (2018), 813-823.
doi: 10.3390/app8050813. |
[33] |
S. Ren, K. He and R. Girshick,
Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, (2015), 91-99.
|
[34] |
M. Simony, S. Milzy and K. Amendey,
Complex-YOLO: An Euler-region-proposal for real-time 3D object detection on point clouds, Proceedings of the European Conference on Computer Vision, 11127 (2018), 197-209.
doi: 10.1007/978-3-030-11009-3_11. |
[35] |
M. Vakalopoulou, K. Karantzalos and N. Komodakis,
Building detection in very high resolution multispectral data with deep learning features, 2015 IEEE International Geoscience and Remote Sensing Symposium, (2015), 1873-1876.
doi: 10.1109/IGARSS.2015.7326158. |
[36] |
K. S. Willis,
Remote sensing change detection for ecological monitoring in United States protected areas, Biological Conservation, 182 (2015), 233-242.
doi: 10.1016/j.biocon.2014.12.006. |
[37] |
J. Yan, H. Wang and M. Yan,
IoU-adaptive deformable R-CNN: Make full use of iou for multi-class object detection in remote sensing imagery, Remote Sensing, (2019), 286-306.
|
[38] |
Y. Zhong, X. Han and L. Zhang,
Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 138 (2018), 281-294.
doi: 10.1016/j.isprsjprs.2018.02.014. |
[39] |
H. Zhu, X. Chen and W. Dai,
Orientation robust object detection in aerial images using deep convolutional neural network, 2015 IEEE International Conference on Image Processing, (2015), 3735-3739.
doi: 10.1109/ICIP.2015.7351502. |
show all references
References:
[1] |
U. R. Acharya, H. Fujita and S. Bhat,
Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images, Information Fusion, (2016), 32-39.
doi: 10.1016/j.inffus.2015.09.006. |
[2] |
H. Bay, T. Tuytelaars and L. Van Gool,
Surf: Speeded up robust features, European Conference On Computer Vision, 3951 (2006), 404-417.
doi: 10.1007/11744023_32. |
[3] |
Y. S. Cao, X. Niu and Y. Dou, Region-based convolutional neural networks for object detection in very high resolution remote sensing images, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, (2016), 548–554.
doi: 10.1109/FSKD.2016.7603232. |
[4] |
K. Chatfield, K. Simonyan and A. Vedaldi, Return of the devil in the details: Delving deep into convolutional nets, proceedings of BMVC, (2014).
doi: 10.5244/C.28.6. |
[5] |
L. C. Chen, G. Papandreou and I. Kokkinos, Semantic image segmentation with deep convolutional nets and fully connected crfs, arXiv: 1412.7062. |
[6] |
G. Cheng, P. Zhou and J. Han,
Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 54 (2016), 7405-7415.
doi: 10.1109/TGRS.2016.2601622. |
[7] |
J. Dai, Y. Li, K. He and J. Sun,
R-fcn: Object detection via region-based fully convolutional networks, Advances in Neural Information Processing Systems, (2016), 379-387.
|
[8] |
N. Dalal and B. Triggs,
Histograms of oriented gradients for human detection, international Conference on Computer Vision & Pattern Recognition, (2005), 886-893.
doi: 10.1109/CVPR.2005.177. |
[9] |
R. Girshick, J. Donahue, T. Darrell and J. Malik,
Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2014), 580-587.
doi: 10.1109/CVPR.2014.81. |
[10] |
R. Girshick,
Fast R-CNN, Proceedings of the IEEE International Conference on Computer Vision, (2015), 1440-1448.
doi: 10.1109/ICCV.2015.169. |
[11] |
D. Gray and H. Tao,
Viewpoint invariant pedestrian recognition with an ensemble of localized features, Proceedings of the European Conference on Computer Vision, 5302 (2008), 262-275.
doi: 10.1007/978-3-540-88682-2_21. |
[12] |
X. Han, Y. Zhong and L. Zhang,
An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery, Remote Sensing, 9 (2017), 666-687.
doi: 10.3390/rs9070666. |
[13] |
K. He, X. Zhang, S. Ren and J. Sun,
Spatial pyramid pooling in deep convolutional networks for visual recognition, ECCV, 8591 (2014), 346-361.
doi: 10.1007/978-3-319-10578-9_23. |
[14] |
K. He, G. Gkioxari, P. Dollár and R. Girshick,
Mask r-cnn, Proceedings of the IEEE international conference on computer vision, (2017), 2961-2969.
doi: 10.1109/ICCV.2017.322. |
[15] |
J. Jeong, H. Park and N. Kwak,
Enhancement of SSD by concatenating feature maps for object detection, BMVC, (2017), 1-12.
doi: 10.5244/C.31.76. |
[16] |
K. Kanistras, G. Martins and M. J. Rutherford,
Survey of unmanned aerial vehicles (UAVs) for traffic monitoring, Handbook of Unmanned Aerial Vehicles, (2016), 2643-2666.
doi: 10.1109/ICUAS.2013.6564694. |
[17] |
M. Kang, K. Ji and X. Leng,
Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection, Remote Sensing, (2017), 860-873.
|
[18] |
Y. Ke and R. Sukthankar,
PCA-SIFT: A more distinctive representation for local image descriptors, CVPR, (2004), 506-513.
|
[19] |
S. Khanal, J. Fulton and S. Shearer,
An overview of current and potential applications of thermal remote sensing in precision agriculture, Computers and Electronics in Agriculture, 139 (2017), 22-32.
doi: 10.1016/j.compag.2017.05.001. |
[20] |
V. Kyrki, J. K. Kamarainen and H. Kälviäinen,
Simple Gabor feature space for invariant object recognition, Pattern Recognition Letters, 25 (2004), 311-318.
doi: 10.1016/j.patrec.2003.10.008. |
[21] |
Y. Li, Y. Tan and J. Deng,
Cauchy graph embedding optimization for built-up areas detection from high-resolution remote sensing images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (2015), 2078-2096.
doi: 10.1109/JSTARS.2015.2394504. |
[22] |
W. Liu, D. Anguelov and D. Erhan,
Ssd: Single shot multibox detector, European Conference on Computer Vision, 9905 (2016), 21-37.
doi: 10.1007/978-3-319-46448-0_2. |
[23] |
D. G. Lowe,
Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60 (2004), 91-110.
doi: 10.1023/B:VISI.0000029664.99615.94. |
[24] |
J. Ma, H. Zhou and J. Zhao,
Robust feature matching for remote sensing image registration via locally linear transforming, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 6469-6481.
doi: 10.1109/TGRS.2015.2441954. |
[25] |
M. Mazhar Rathore, A. Ahmad and A. Paul,
Urban planning and building smart cities based on the internet of things using big data analytics, Computer Networks, 101 (2016), 63-80.
doi: 10.1016/j.comnet.2015.12.023. |
[26] |
B. S. Manjunath, J. R. Ohm and V. V. Vasudevan,
Color and texture descriptors, IEEE Transactions on Circuits and Systems for Video Technology, 11 (2011), 703-715.
doi: 10.1109/76.927424. |
[27] |
V. Nair and G. E. Hinton,
3D object recognition with deep belief nets, Advances in Neural Information Processing Systems, (2009), 1339-1347.
|
[28] |
H. Noh, S. Hong and B. Han,
Learning deconvolution network for semantic segmentation, Proceedings of the IEEE International Conference on Computer Vision, (2015), 1520-1528.
doi: 10.1109/ICCV.2015.178. |
[29] |
W. Ouyang, X. Wang and X. Zeng,
Deepid-net: Deformable deep convolutional neural networks for object detection, The IEEE Conference on Computer Vision and Pattern Recognition, (2015), 2403-2412.
doi: 10.1109/CVPR.2015.7298854. |
[30] |
M. T. Pham, G. Mercier and O. Regniers,
Texture retrieval from VHR optical remote sensed images using the local extrema descriptor with application to vineyard parcel detection, Remote Sensing, 8 (2016), 368-388.
doi: 10.3390/rs8050368. |
[31] |
J. Redmon, S. Divvala and R. Girshick,
You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 779-788.
doi: 10.1109/CVPR.2016.91. |
[32] |
Y. Ren, C. Zhu and S. Xiao,
Small object detection in optical remote sensing images via modified faster R-CNN, Applied Sciences, 8 (2018), 813-823.
doi: 10.3390/app8050813. |
[33] |
S. Ren, K. He and R. Girshick,
Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, (2015), 91-99.
|
[34] |
M. Simony, S. Milzy and K. Amendey,
Complex-YOLO: An Euler-region-proposal for real-time 3D object detection on point clouds, Proceedings of the European Conference on Computer Vision, 11127 (2018), 197-209.
doi: 10.1007/978-3-030-11009-3_11. |
[35] |
M. Vakalopoulou, K. Karantzalos and N. Komodakis,
Building detection in very high resolution multispectral data with deep learning features, 2015 IEEE International Geoscience and Remote Sensing Symposium, (2015), 1873-1876.
doi: 10.1109/IGARSS.2015.7326158. |
[36] |
K. S. Willis,
Remote sensing change detection for ecological monitoring in United States protected areas, Biological Conservation, 182 (2015), 233-242.
doi: 10.1016/j.biocon.2014.12.006. |
[37] |
J. Yan, H. Wang and M. Yan,
IoU-adaptive deformable R-CNN: Make full use of iou for multi-class object detection in remote sensing imagery, Remote Sensing, (2019), 286-306.
|
[38] |
Y. Zhong, X. Han and L. Zhang,
Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 138 (2018), 281-294.
doi: 10.1016/j.isprsjprs.2018.02.014. |
[39] |
H. Zhu, X. Chen and W. Dai,
Orientation robust object detection in aerial images using deep convolutional neural network, 2015 IEEE International Conference on Image Processing, (2015), 3735-3739.
doi: 10.1109/ICIP.2015.7351502. |






Label | Name | Total instances | Complete instances | Fragmentary instances | Scene class | Images | Image width | Sub-images |
1 | aircraft | 103917 | 85975 | 17942 | 203 | 2858 | 6000-15000 | 62129 |
2 | ship | 68436 | 54386 | 14050 | 30 | 397 | 5000-18000 | 53860 |
Label | Name | Total instances | Complete instances | Fragmentary instances | Scene class | Images | Image width | Sub-images |
1 | aircraft | 103917 | 85975 | 17942 | 203 | 2858 | 6000-15000 | 62129 |
2 | ship | 68436 | 54386 | 14050 | 30 | 397 | 5000-18000 | 53860 |
Label | Scale(pixels) | Images | Instances | Sub-images |
aircraft | 5 | 272 | 980 | |
ship | 5 | 225 | 980 |
Label | Scale(pixels) | Images | Instances | Sub-images |
aircraft | 5 | 272 | 980 | |
ship | 5 | 225 | 980 |
Input Size | Per Batch Size | Max Iteration | Anchor Stride | Base Learning Rate | Steps | Weight Decay | NMS Threshold | Momentum |
600 | 8 | 90000 | (4, 8, 16, 32, 64) | 0.01 | (60000, 80000) | 0.0001 | 0.7 | 0.9 |
Input Size | Per Batch Size | Max Iteration | Anchor Stride | Base Learning Rate | Steps | Weight Decay | NMS Threshold | Momentum |
600 | 8 | 90000 | (4, 8, 16, 32, 64) | 0.01 | (60000, 80000) | 0.0001 | 0.7 | 0.9 |
Cropping Size | AP | Cost time(s) |
C300 | 0.430 | 45.82 |
C600 | 0.651 | 13.20 |
C800 | 0.647 | 8.79 |
Cropping Size | AP | Cost time(s) |
C300 | 0.430 | 45.82 |
C600 | 0.651 | 13.20 |
C800 | 0.647 | 8.79 |
Method | IoU=0.5 | IoU=0.75 | ||||||||||
TP | FP | FN | Recall | Precision | AP | TP | FP | FN | Recall | Precision | AP | |
YOLOv3 | 213 | 25 | 59 | 0.783 | 0.895 | 0.727 | 166 | 72 | 106 | 0.610 | 0.6974 | 0.494 |
Faster R-CNN | 242 | 55 | 30 | 0.890 | 0.815 | 0.830 | 189 | 108 | 83 | 0.695 | 0.636 | 0.618 |
Mask R-CNN | 245 | 38 | 27 | 0.901 | 0.866 | 0.843 | 184 | 99 | 88 | 0.676 | 0.650 | 0.570 |
Big Map R-CNN | 261 | 4 | 11 | 0.960 | 0.985 | 0.959 | 241 | 24 | 31 | 0.886 | 0.909 | 0.850 |
Method | IoU=0.5 | IoU=0.75 | ||||||||||
TP | FP | FN | Recall | Precision | AP | TP | FP | FN | Recall | Precision | AP | |
YOLOv3 | 213 | 25 | 59 | 0.783 | 0.895 | 0.727 | 166 | 72 | 106 | 0.610 | 0.6974 | 0.494 |
Faster R-CNN | 242 | 55 | 30 | 0.890 | 0.815 | 0.830 | 189 | 108 | 83 | 0.695 | 0.636 | 0.618 |
Mask R-CNN | 245 | 38 | 27 | 0.901 | 0.866 | 0.843 | 184 | 99 | 88 | 0.676 | 0.650 | 0.570 |
Big Map R-CNN | 261 | 4 | 11 | 0.960 | 0.985 | 0.959 | 241 | 24 | 31 | 0.886 | 0.909 | 0.850 |
Method | IoU=0.5 | IoU=0.75 | ||||||||||
TP | FP | FN | Recall | Precision | AP | TP | FP | FN | Recall | Precision | AP | |
YOLOv3 | 128 | 53 | 97 | 0.569 | 0.707 | 0.513 | 66 | 115 | 159 | 0.293 | 0.365 | 0.213 |
Faster R-CNN | 164 | 185 | 61 | 0.729 | 0.470 | 0.651 | 78 | 271 | 147 | 0.347 | 0.223 | 0.259 |
Mask R-CNN | 166 | 121 | 59 | 0.738 | 0.578 | 0.661 | 78 | 209 | 147 | 0.347 | 0.272 | 0.273 |
Big Map R-CNN | 191 | 49 | 34 | 0.849 | 0.796 | 0.826 | 133 | 107 | 92 | 0.591 | 0.554 | 0.546 |
Method | IoU=0.5 | IoU=0.75 | ||||||||||
TP | FP | FN | Recall | Precision | AP | TP | FP | FN | Recall | Precision | AP | |
YOLOv3 | 128 | 53 | 97 | 0.569 | 0.707 | 0.513 | 66 | 115 | 159 | 0.293 | 0.365 | 0.213 |
Faster R-CNN | 164 | 185 | 61 | 0.729 | 0.470 | 0.651 | 78 | 271 | 147 | 0.347 | 0.223 | 0.259 |
Mask R-CNN | 166 | 121 | 59 | 0.738 | 0.578 | 0.661 | 78 | 209 | 147 | 0.347 | 0.272 | 0.273 |
Big Map R-CNN | 191 | 49 | 34 | 0.849 | 0.796 | 0.826 | 133 | 107 | 92 | 0.591 | 0.554 | 0.546 |
Method | Backbone | AP( |
Mask R-CNN | ResNet50 | 75.2 |
Big Map R-CNN | ResNet50 | 89.2 |
Method | Backbone | AP( |
Mask R-CNN | ResNet50 | 75.2 |
Big Map R-CNN | ResNet50 | 89.2 |
Method | mAP (IoU=0.5) | mAP (IoU=0.75) | Inference time(s/im) |
YOLOv3 | 0.620 | 0.354 | 3.310 |
Faster R-CNN | 0.741 | 0.439 | 13.254 |
Mask R-CNN | 0.752 | 0.422 | 13.310 |
Big Map R-CNN | 0.892 | 0.700 | 16.005 |
Method | mAP (IoU=0.5) | mAP (IoU=0.75) | Inference time(s/im) |
YOLOv3 | 0.620 | 0.354 | 3.310 |
Faster R-CNN | 0.741 | 0.439 | 13.254 |
Mask R-CNN | 0.752 | 0.422 | 13.310 |
Big Map R-CNN | 0.892 | 0.700 | 16.005 |
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