Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Fukun BI; Mingyang LEI; Zhihua YANG; Jinyuan HOU; Yanyan QIN

doi:10.1007/s13320-019-0558-5

[1] M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios, “Building detection in very high resolution multispectral data with deep learning features,” in Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015, pp. 1873–1876.

[2] S. Ghaffarian and S. Ghaffarian, “Automatic building detection based on purposive fastICA (PFICA) algorithm using monocular high resolution Google earth images,” ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 97: 152-159.

[3] A. Manno-Kovács and A. O. Ok, “Building detection from monocular VHR images by integrated urban area knowledge,” IEEE Geoscience and Remote Sensing Letters, 2015, 12(10): 2140-2144.

[4] X. Yao, J. Han, L. Guo, S. H. Bu, and Z. B. Liu, “A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF,” Neurocomputing, 2015, 164: 162-172.

[5] A. O. Ok and E. Baseski, “Circular oil tank detection from panchromatic satellite images: a new automated approach,” IEEE Geoscience and Remote Sensing Letters, 2015, 12(6): 1347-1351.

[6] L. Zhang, Z. Shi, and J. Wu, “A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(10): 4895-4909.

[7] B. Sirmacek and C. Unsalan, “Urban area detection using local feature points and spatial voting,” IEEE Geoscience and Remote Sensing Letters, 2010, 7(1): 146-150.

[8] C. Tao, Y. Tan, H. Cai, and J. W. Tian, “Airport detection from large IKONOS images using clustered SIFT keypoints and region information,” IEEE Geoscience and Remote Sensing Letters, 2011, 8(1): 128-132.

[9] D. G. Lowe, “Distinctive image features from scale-invariant keypoint,” International Journal of Computer Vision, 2004, 60(2): 91-110.

[10] D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond, and D. Schmalstieg, “Real-time detection and tracking for augmented reality on mobile phones,” IEEE Transactions on Visualization and Computer Graphics, 2010, 16(3): 355-368.

[11] P. Apostolos and N. Christos, “Vehicle logo recognition using a sift-based enhanced matching schem,” IEEE Transaction on Intelligent Transportation Systems, 2010, 11(2): 322-328.

[12] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Ohio, United States, 2014, pp. 580-587.

[13] D. Dai, W. Yang, and H. Sun, “Multilevel local pattern histogram for SAR image classification,” IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 225-229.

[14] M. A. Hearst, S. T. Dumais, E. Osuna, J. C. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and Their Applications, 1998, 13(4): 18-28.

[15] Y. Guerbai, Y. Chibani, and B. Hadjadji, “The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters,” Pattern Recognition, 2015, 48(1): 103-113.

[16] Y. Chen, X. S. Zhou, and T. S. Huang, “One-class SVM for learning in image retrieval,” in Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001, pp. 34-37.

[17] J. Mu-oz-Marí, F. Bovolo, L. Gómez-Chova, L. Bruzzone, and G. Camp-Valls, “Semisupervised one-class support vector machines for classification of remote sensing data,” IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(8): 3188-3197.

[18] S. Nam, W. Kim, S. Mun, J. Hou, S. Choi, and H. Lee, “A SIFT features based blind watermarking for DIBR 3D images,” Multimedia Tools and Applications, 2018, 77(7): 7811-7850.

[19] H. Bay, T. Tuvtellars, and G. L. Van, “SURF: speeded up robust features,” in Proceedings of the European Conference on Computer Vision, Graz, Austria, 2006, pp. 404-417.

[20] E. Rosten, R. Porter, and T. Drummond, “Faster and better: a machine learning approach to corner detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119.

[21] R. Velazquez-Pupo, A. Sierra-Romero, D. Torres-Roman, Y. V. Shkvarko, J. Santiago-Paz, and D. Gomez-Gutierrez, “Vehicle detection with occlusion handling, tracking, and OC-SVM classification: a high performance vision-based system,” Sensors, 2018, 18(2): 374-396.

[22] H. Chaabouni-Chouayakh and M. Datcu, “Coarse-to-fine approach for urban area interpretation using TerraSAR-X data,” IEEE Geoscience and Remote Sensing Letters, 2010, 7(1): 78-82.

[23] B. Sirmacek and C. Unsalan, “Urban-area and building detection using SIFT keypoints and graph theory,” IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4): 1156-1167.

[24] J. Niemeyer, F. Rottensteiner, and U. Soergel, “Contextual classification of lidar data and building object detection in urban areas,” ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 152-165.

[25] G. Cheng and J. Han, “A survey on object detection in optical remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117: 11-28.

[26] D. Zhu, B. Wang, and L. Zhang, “Airport target detection in remote sensing images: a new method based on two-way saliency,” IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1096-1100.

[27] B. Hou, Y. Wang, and Q. Liu, “A saliency guided semi-supervised building change detection method for high resolution remote sensing images,” Sensors, 2016, 16(9): 1377-1398.

[28] Y. Hu, X. Hu, PL Li, and Y. Ding, “Building detection from orthophotos using binary feature classification,” Multimedia Tools and Applications, 2018, 77(3): 3339-3351.

[29] N. Chandra and J. Ghosh, “A cognitive viewpoint on building detection from remotely sensed multispectral images,” IETE Journal of Research, 2018, 64(2): 165-175.

[30] E. Rosten and T. Drummond, “Machine learning for high-speed corner detection,” in Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 2006, pp. 430-443.

[31] L. Zheng, Y. Yang, and Q. Tian, “SIFT meets CNN: a decade survey of instance retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(5): 1224-1244.

[32] Y. Yu and J. Yang, “Visual saliency using binary spectrum of Walsh-Hadamard transform and its applications to ship detection in multispectral imagery,” Neural Processing Letters, 2017, 45(3): 759-776.

[33] T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041.

[34] Z. Wang, B. Fan, and F. Wu, “Local intensity order pattern for feature description,” in Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 603-610.

[35] G. Cheng, J. W. Han, P. C. Zhou, and D. Xu, “Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection,” IEEE Transactions on Image Processing, 2019, 28(1): 265-278.

[36] G. Cheng, P. C. Zhou, and J. W. Han, “Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7405-7415.