|
|
- 2018
基于ImageNet预训练卷积神经网络的遥感图像检索
|
Abstract:
高分辨率遥感图像内容复杂，细节信息丰富，传统的浅层特征在描述这类图像上存在一定难度，容易导致检索中存在较大的语义鸿沟。本文将大规模数据集ImageNet上预训练的4种不同卷积神经网络用于遥感图像检索，首先分别提取4种网络中不同层次的输出值作为高层特征，再对高层特征进行高斯归一化，然后采用欧氏距离作为相似性度量进行检索。在UC-Merced和WHU-RS数据集上的一系列实验结果表明，4种卷积神经网络的高层特征中，以CNN-M特征的检索性能最好；与视觉词袋和全局形态纹理描述子这两种浅层特征相比，高层特征的检索平均准确率提高了15.7%~25.6%，平均归一化修改检索等级减少了17%~22.1%。因此将ImageNet上预训练的卷积神经网络用于遥感图像检索是一种有效的方法
| [1] | Aptoula E. Remote Sensing Image Retrieval with Global Morphological Texture Descriptors[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2014, 52(5):3023-3034 |
| [2] | Bretschneider T, Cavet R, Kao O. Retrieval of Remotely Sensed Imagery Using Spectral Information Content[C]. The 22nd IEEE International Conference of Geoscience and Remote Sensing Symposium, Toronto, Canada, 2002 |
| [3] | Scott G, Klaric M, Davis C, et al. Entropy-Balanced Bitmap Tree for Shape-based Object Retrieval from Large-Scale Satellite Imagery Databases[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2011, 49(5):1603-1616 |
| [4] | Demir B, Bruzzone L. A Novel Active Learning Method in Relevance Feedback for Content-based Remote Sensing Image Retrieval[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2015, 53(9):2323-2334 |
| [5] | Yang Y, Newsam S. Geographic Image Retrieval Using Local Invariant Features[J].<em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2013, 51(2):818-832 |
| [6] | Oquab M, Bottou L, Laptev I, et al. Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks[C]. The 27th IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014 |
| [7] | Babenko A, Slesarev A, Chigorin A, et al. Neural Codes for Image Retrieval[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014 |
| [8] | Vedaldi A, Lenc K. MatConvNet:Convolutional Neural Networks for MATLAB[C]. The 23rd ACM International Conference on Multimedia, Brisbane, Austrialia, 2015 |
| [9] | Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014 |
| [10] | Donahue J, Jia Y, Vinyals O, et al. Decaf:A Deep Convolutional Activation Feature for Generic Visual Recognition[C]. The 31st International Conference on Machine Learning, Beijing, China, 2014 |
| [11] | Chatfield K, Simonyan K, Vedaldi A, et al. Return of the Devil in the Details:Delving Deep into Convolutional Networks[C]. The 25th British Machine Vision Conference, Nottingham, England, 2014 |
| [12] | Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C]. The 3rd International Conference on Learning Representations, San Diego, Canada, 2015 |
| [13] | Penatti O A B, Nogueira K,Santos J A D. Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains?[C]. The IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, 2015 |
| [14] | Ng J, Yang F, Davis L. Exploiting Local Features from Deep Networks for Image[C]. The IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, 2015 |
| [15] | Xia G S, Yang W, Delon J, et al. Structrual High-Resolution Satellite Image Indexing. In Processings of the ISPRS, TC VⅡ Symposium Part A:100 Years ISPRS-Advancing Remote Sensing Science[C]. ISPRS TC Ⅶ Symposium-100 Years ISPRS 38, Vienna, Austria, 2010 |
| [16] | Liu T, Zhang L, Li P, et al. Remotely Sensed Image Retrieval Based on Region-Level Semantic Mining[J].<em>EURASIP Journal on Image and Video Preocessing</em>, 2012, 4(1):1-11 |
| [17] | Yang Jin, Liu Jianbo, Dai Qin. An Improved Remote Sensing Image Retrieval Method Based on Bag of Word Framework[J]. <em>Geomatics and Information Science of Wuhan University</em>, 2014, 39(9):1109-1113(杨进, 刘建波, 戴芹. 一种改进包模型的遥感图像检索方法[J]. 武汉大学学报·信息科学版, 2014, 39(9):1109-1113) |
| [18] | Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[C]. The 26th Conference on Neural Information Processing Systems, Nevada, US, 2012 |
| [19] | Hu F, Xia G S, Hu J, et al.Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery[J]. <em>Remote Sensing.</em> 2015, 7(11):14680-14707 |
| [20] | Yang Y, Newsam S. Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification[C].The 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, US, 2010 |
