利用超像素级上下文特征进行靠岸集装箱船检测
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摘要
高分辨率光学遥感影像中靠岸集装箱船受到岸边建筑、阴影和背景环境的干扰严重,且其船身模式与相邻陆地上集装箱非常相似,较难实现自动化检测。针对这一难题,提出了一种利用超像素级上下文特征进行靠岸集装箱船检测的方法。首先,对图像进行过分割生成超像素,在超像素区域提取颜色、纹理特征并级联邻域超像素特征形成超像素级上下文特征;然后,将目标超像素作为正样本,并自适应地选择较难区分的背景超像素作为负样本来训练分类器,实现对目标、背景超像素的分类;最后,利用全连接条件随机场对分类结果优化,实现对靠岸集装箱船的检测。实验结果表明,该方法能够较为可靠地检测靠岸集装箱船,具有一定的应用前景。
Inshore containers in high resolution optical imagery are under severe interference, such as structure, shadow, and environment, and the ship bodies are very similar to the container structures on nearby land. These situations make the automatic detection of inshore containers a very challenging task. In order to address this problem, this paper proposes a detection method for inshore containers based on the superpixel-level contextual feature. Firstly, the image is segmented into superpixels, and the features of the superpixel and its neighboring superpixels are concatenated into the superpixel-level contextual feature. Then, based on the positive samples and the actively selected negative samples, the target and the background superpixels are classified via machine learning. Finally, the fully connected conditional random field is employed to refine the classification result and realize the detection. The experimental result verifies the applicability of the proposed method.
Inshore containers in high resolution optical imagery are under severe interference, such as structure, shadow, and environment, and the ship bodies are very similar to the container structures on nearby land. These situations make the automatic detection of inshore containers a very challenging task. In order to address this problem, this paper proposes a detection method for inshore containers based on the superpixel-level contextual feature. Firstly, the image is segmented into superpixels, and the features of the superpixel and its neighboring superpixels are concatenated into the superpixel-level contextual feature. Then, based on the positive samples and the actively selected negative samples, the target and the background superpixels are classified via machine learning. Finally, the fully connected conditional random field is employed to refine the classification result and realize the detection. The experimental result verifies the applicability of the proposed method.
引文
[1] Jiang Libing, Wang Zhuang, Hu Weidong. An AIA-C-Based Inshore Ship Target Detection Approach[J].Remote Sensing Technology and Application, 2007,22(1): 88-94 (蒋李兵,王壮,胡卫东. 一种基于可变夹角链码的靠岸舰船目标检测方法[J]. 遥感技术与应用,2007, 22(1): 88-94)
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[12] Levinshtein A, Stere A, Kutulakos K N, et al. Turbopixels: Fast Superpixels Using Geometric Flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2 290-2 297
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[14] Tighe J, Lazebnik S. Superparsing: Scalable Non- parametric Image Parsing with Superpixels[J]. International Journal of Computer Vision, 2010, 101(2): 352-365
[15] Han Bin.Content-Based Superpixel Merging and Its Application in Image Segmentation[D]. Shanghai:Shanghai Jiao Tong University, 2013 (韩斌. 基于内容的超像素合并及其在图像分割中的应用[D]. 上海:上海交通大学,2013)
[16] Yang B, Yan J, Lei Z, et al. Aggregate Channel Features for Multi-view Face Detection[C]. Biometrics (IJCB), Florida, USA, 2014
[17] Liu Zhongwei, Zhang Yujin. A Comparitive and Analysis Study of Ten Color Feature-Based Image Retrieval Algorithms[J]. Signal Processing, 2000, 16(1): 79-84 (刘忠伟, 章毓晋. 十种基于颜色特征图像检索算法的比较和分析[J]. 信号处理, 2000, 16(1): 79-84)
[18] Ojala T, Pietikainen M, Maenpaa T. Multi-resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2002,24(7): 971-987
[19] Chen Rong, Cao Yongfeng, Sun Hong. Multi-class Image Classification with Active Learning and Semi-supervised Learning[J]. Acta Automatica Sinica,2011, 37(8): 954-962 (陈荣, 曹永锋, 孙洪. 基于主动学习和半监督学习的多类图像分类[J]. 自动化学报, 2011, 37(8): 954-962)
[20] Chang C C, Lin C J. LIBSVM: A Library for Su-pport Vector Machines, 2011[OL]. http://www.csie. ntu.edu.tw/～cjlin/libsvm/, 2012
[2] Xu J, Sun X, Zhang D, et al. Automatic Detection of Inshore Ships in High-resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform[J]. IEEE Geoscience and Remote Sen- sing Letters, 2014, 11(12): 2 070-2 074
[3] Liu G, Zhang Y, Zheng X, et al. A New Method on Inshore Ship Detection in High-resolution Satellite Images Using Shape and Context Information[J]. IEEE Geoscience and Remote Sensing Letters,2014, 11(3): 617-621
[4] Vargas J E, Falcao A X, dos Santos J A, et al. Contextual Superpixel Description for Remote Sen- sing Image Classification[C]. IEEE Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015
[5] Carbonell J G, Michalski R S, Mitchell T M. An Overview of Machine Learning[M]. Berlin, Heidelberg: Springer, 1983: 3-23
[6] Tong S, Chang E. Support Vector Machine ActiveLearning for Image Retrieval[C]. The ACM International Conference on Multi-media, Ottawa, Canada, 2001
[7] Kr?henbühl P, Koltun V. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials[J]. Adv Neural Inf Process Syst, 2011, 24: 109-117
[8] Achanta R, Shaji A, Smith K, et al. SLIC Super- pixels Compared to State-of-the-Art Superpixel Methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2 274-2 282
[9] Cortes C ,Vapnik V. Support Vector Networks[J]. Machine Learning, 1995,20(3): 273-297
[10] Wang Chunyao, Chen Junzhou, Li Wei. Review onSuperpixel Segmentation Algorithms[J]. Application Research of Computers, 2014, 31(1): 6-12 (王春瑶,陈俊周, 李炜. 超像素分割算法研究综述[J]. 计算机应用研究, 2014, 31(1): 6-12)
[11] Comaniciu D, Meer P. MeanShift: A Robust Approach Toward Feature Space Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619
[12] Levinshtein A, Stere A, Kutulakos K N, et al. Turbopixels: Fast Superpixels Using Geometric Flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2 290-2 297
[13] van den Bergh M, Boix X, Roig G, et al. SEEDS:Superpixels Extracted via Energy-Driven Sampling[J]. International Journal of Computer Vision, 2015, 111(3): 298-314
[14] Tighe J, Lazebnik S. Superparsing: Scalable Non- parametric Image Parsing with Superpixels[J]. International Journal of Computer Vision, 2010, 101(2): 352-365
[15] Han Bin.Content-Based Superpixel Merging and Its Application in Image Segmentation[D]. Shanghai:Shanghai Jiao Tong University, 2013 (韩斌. 基于内容的超像素合并及其在图像分割中的应用[D]. 上海:上海交通大学,2013)
[16] Yang B, Yan J, Lei Z, et al. Aggregate Channel Features for Multi-view Face Detection[C]. Biometrics (IJCB), Florida, USA, 2014
[17] Liu Zhongwei, Zhang Yujin. A Comparitive and Analysis Study of Ten Color Feature-Based Image Retrieval Algorithms[J]. Signal Processing, 2000, 16(1): 79-84 (刘忠伟, 章毓晋. 十种基于颜色特征图像检索算法的比较和分析[J]. 信号处理, 2000, 16(1): 79-84)
[18] Ojala T, Pietikainen M, Maenpaa T. Multi-resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2002,24(7): 971-987
[19] Chen Rong, Cao Yongfeng, Sun Hong. Multi-class Image Classification with Active Learning and Semi-supervised Learning[J]. Acta Automatica Sinica,2011, 37(8): 954-962 (陈荣, 曹永锋, 孙洪. 基于主动学习和半监督学习的多类图像分类[J]. 自动化学报, 2011, 37(8): 954-962)
[20] Chang C C, Lin C J. LIBSVM: A Library for Su-pport Vector Machines, 2011[OL]. http://www.csie. ntu.edu.tw/～cjlin/libsvm/, 2012