Accurate object localization in remote sensing images based on convolutional neural networks

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 5, 2017, Pages 2486-2498

  Long, Yang ^a   Gong, Yiping ^a   Xiao, Zhifeng ^a   Liu, Qing ^b  

^a WUHAN UNIVERSITY   (China)

^b Shenzhen NARI Technologies Co Ltd   (China)

Author keywords

Convolutional neural network (CNN); object localization; remote sensing images; unsupervised score based bounding box regression (USB BBR)

Indexed keywords

COMPLEX NETWORKS; CONVOLUTION; GRAPHIC METHODS; IMAGE RECONSTRUCTION; NEURAL NETWORKS; OBJECT RECOGNITION; REMOTE SENSING;

CONTEXT INFORMATION; CONVOLUTIONAL NEURAL NETWORK; DIMENSION REDUCTION MODEL; HIGH RESOLUTION REMOTE SENSING IMAGES; HISTOGRAM OF ORIENTED GRADIENTS; LOCAL BINARY PATTERNS; NON-MAXIMUM SUPPRESSION; REMOTE SENSING IMAGES;

OBJECT DETECTION;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; HISTOGRAM; IMAGE ANALYSIS; PRECISION; REGRESSION ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY; UNSUPERVISED CLASSIFICATION;

EID: 85010214408     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2645610     Document Type: Article

References (51)

1. J. Han, D. Zhang, G. Cheng, L. Guo, and J. Ren, "Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 6, pp. 3325-3337, Jun. 2015.
2. W. Shao, W. Yang, G. Liu, and J. Liu, "Car detection from highresolution aerial imagery using multiple features," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2012, pp. 4379-4382.
3. F. Zhang, B. Du, and L. Zhang, "Saliency-guided unsupervised feature learning for scene classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 2175-2184, Apr. 2015.
4. J. Han et al., "Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding," ISPRS J. Photogram. Remote Sens., vol. 89, pp. 37-48, Mar. 2014.
5. G. Cheng, P. Zhou, and J. Han, "Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7405-7415, Dec. 2016.
6. Z. Xiao, Q. Liu, G. Tang, and X. Zhai, "Elliptic Fourier transformationbased histograms of oriented gradients for rotationally invariant object detection in remote-sensing images," Int. J. Remote Sens., vol. 36, no. 2, pp. 618-644, 2014.
7. G. Cheng and J. Han, "A survey on object detection in optical remote sensing images," ISPRS J. Photogram. Remote Sens., vol. 117, pp. 11-28, Mar. 2016.
8. W. Zhang, X. Sun, K. Fu, C. Wang, and H. Wang, "Object detection in high-resolution remote sensing images using rotation invariant parts based model," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 74-78, Jan. 2014.
9. Y. Huang, L. Zhang, P. Li, and Y. Zhong, "High-resolution hyper-spectral image classification with parts-based feature and morphology profile in urban area," Geo-Spatial Inf. Sci., vol. 13, no. 2, pp. 111-122, 2010.
10. G. Cheng, J. Han, P. Zhou, and L. Guo, "Multi-class geospatial object detection and geographic image classification based on collection of part detectors," ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119-132, Dec. 2014.
11. G. Cheng, J. Han, L. Guo, Z. Liu, S. Bu, and J. Ren, "Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4238-4249, Aug. 2015.
12. Y. Li, X. Sun, H. Wang, H. Sun, and X. Li, "Automatic target detection in high-resolution remote sensing images using a contour-based spatial model," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 5, pp. 886-890, Sep. 2012.
13. H. Sun, X. Sun, H. Wang, Y. Li, and X. Li, "Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 1, pp. 109-113, Jan. 2012.
14. G. Cheng, L. Guo, T. Zhao, J. Han, H. Li, and J. Fang, "Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA," Int. J. Remote Sens., vol. 34, no. 1, pp. 45-59, 2013.
15. X. Yao, J. Han, G. Cheng, L. Guo, and X. Qian, "Semantic annotation of high-resolution satellite images via weakly supervised learning," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 1-12, Jun. 2016.
16. Y. Zhong, F. Fei, and L. Zhang, "Large patch convolutional neural networks for the scene classification of high spatial resolution imagery," J. Appl. Remote Sens., vol. 10, no. 2, p. 025006, 2016.
17. P. Agrawal, R. Girshick, and J. Malik, "Analyzing the performance of multilayer neural networks for object recognition," in Proc. Eur. Conf. Comput. Vis., 2014, pp. 329-344.
18. H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson, "Factors of transferability for a generic convnet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 9, pp. 1790-1802, Sep. 2015.
19. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: An astounding baseline for recognition," in Proc. CVPR, Jun. 2014, pp. 806-813.
20. L. Bottou, "Stochastic gradient descent tricks," Neural Netw., Tricks Trade, vol. 1, no. 1, pp. 421-436, 2012.
21. Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proc. Int. Conf. Mach. Learn., 2010, pp. 111-118.
22. G. E. Dahl, T. N. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), May 2013, pp. 8609-8613.
23. A. Grubb and J. A. Bagnell, "Stacked training for overfitting avoidance in deep networks," in Proc. Int. Conf. Mach. Learn., Oct. 2013, p. 1.
24. A. G. Howard. "Some improvements on deep convolutional neural network based image classification." Unpublished paper, 2013. [Online]. Available: https://arxiv.org/abs/1312.5402
25. A. Mahendran and A. Vedaldi. "Understanding deep image representations by inverting them." Unpublished paper, 2014. [Online]. Available: https://arxiv.org/abs/1412.0035
26. I. Sutskever, J. Martens, G. Dahl, and G. Hinton, "On the importance of initialization and momentum in deep learning," in Proc. Int. Conf. Mach. Learn., May 2013, pp. 1139-1147.
27. J. Dean et al., "Large scale distributed deep networks," in Proc. Neural Inf. Process. Syst. (NIPS), 2012, pp. 1-11.
28. A. Krizhevsky. (2014). "One weird trick for parallelizing convolutional neural networks." [Online]. Available: https://arxiv.org/abs/1404.5997
29. T. Paine, H. Jin, J. Yang, Z. Lin, and T. Huang. "GPU asynchronous stochastic gradient descent to speed up neural network training." Unpublished paper, 2013. [Online]. Available: https://arxiv.org/abs/1312.6186
30. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Red Hook, NY, USA: Curran Associates, Inc., 2012, pp. 1097-1105.
31. C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 1-9.
32. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proc. Int. Conf. Learn. Represent. (ICLR), Sep. 2015, pp. 1-14.
33. K. He, X. Zhang, S. Ren, and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 1904-1916, Sep. 2015.
34. K. He, X. Zhang, S. Ren, and J. Sun. "Deep residual learning for image recognition." Unpublished paper, 2015. [Online]. Available: https://arxiv.org/abs/1512.03385
35. P. Zhou, D. Zhang, G. Cheng, and J. Han, "Negative bootstrapping for weakly supervised target detection in remote sensing images," in Proc. IEEE Int. Conf. Multimedia Big Data, Apr. 2015, pp. 318-323.
36. F. Hu, G.-S. Xia, J. Hu, and L. Zhang, "Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery," Remote Sens., vol. 7, no. 11, pp. 14680-14707, 2015.
37. A.-B. Salberg, "Detection of seals in remote sensing images using features extracted from deep convolutional neural networks," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2015, pp. 1893-1896.
38. L. Zhang, Z. Shi, and J. Wu, "A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 10, pp. 1-15, Oct. 2015.
39. T. Ishii, R. Nakamura, H. Nakada, Y. Mochizuki, and H. Ishikawa, "Surface object recognition with CNN and SVM in Landsat 8 images," in Proc. IAPR Int. Conf. Mach. Vis. Appl., May 2015, pp. 3-6.
40. H. Wu, H. Zhang, J. Zhang, and F. Xu, "Typical target detection in satellite images based on convolutional neural networks," in Proc. IEEE Int. Conf. Syst., Man, Oct. 2015, pp. 2956-2961.
41. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
42. Q. Jiang, L. Cao, M. Cheng, C. Wang, and J. Li, "Deep neural networks-based vehicle detection in satellite images," in Proc. Int. Symp. Bioelectron. Bioinf., Oct. 2015, pp. 184-187.
43. H. Zhu, X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, "Orientation robust object detection in aerial images using deep convolutional neural network," in Proc. IEEE Int. Conf. Image Process., Sep. 2015, pp. 3735-3739.
44. J. Ding, B. Chen, H. Liu, and M. Huang, "Convolutional neural network with data augmentation for SAR target recognition," IEEE Geosci. Remote Sens. Lett., vol. 13, no. 3, pp. 364-368, Mar. 2016.
45. I. Ševo and A. Avramovíc, "Convolutional neural network based automatic object detection on aerial images," IEEE Geosci. Remote Sens. Lett., vol. 13, no. 5, pp. 740-744, May 2016.
46. F. Zhang, B. Du, L. Zhang, and M. Xu, "Weakly supervised learning based on coupled convolutional neural networks for aircraft detection," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 9, pp. 5553-5563, Sep. 2016.
47. J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, "Selective search for object recognition," Int. J. Comput. Vis., vol. 104, no. 2, pp. 154-171, Apr. 2013.
48. J. Hosang, R. Benenson, and B. Schiele. "How good are detection proposals, really?" Unpublished paper, 2014. [Online]. Available: https://arxiv.org/abs/1406.6962
49. Tianditu, accessed on Feb. 1, 2016. [Online]. Available: http://map.tianditu.com/map/index.html
50. Y. Jia et al. "Caffe: Convolutional architecture for fast feature embedding." Unpublished paper, 2014. [Online]. Available: https://arxiv.org/abs/1408.5093
51. G. Zhao, T. Ahonen, J. Matas, and M. Pietikäinen, "Rotation-invariant image and video description with local binary pattern features," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1465-1477, Apr. 2012.