Optical remote sensing image retrieval based on convolutional neural networks

Yu LI; Xue-ying LIU; Hong-qun ZHANG; Xiang-juan LI; Xiao-yao SUN

doi:10.3788/OPE.20182601.0200

您当前的位置：

首页 >

文章列表页 >

Optical remote sensing image retrieval based on convolutional neural networks

Information Sciences | 更新时间：2020-08-13

- Optical remote sensing image retrieval based on convolutional neural networks
- Optics and Precision Engineering Vol. 26, Issue 1, Pages: 200-207(2018)
- 作者机构：
  
  1.中国科学院遥感与数字地球研究所, 北京 100094
  2.西安石油大学, 陕西西安 710065
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182601.0200
  CLC： TP751
- Received：09 June 2017，
  
  Accepted：15 August 2017，
  
  Published：25 January 2018
- 稿件说明：
移动端阅览
Yu LI, Xue-ying LIU, Hong-qun ZHANG, et al. Optical remote sensing image retrieval based on convolutional neural networks[J]. Optics and precision engineering, 2018, 26(1): 200-207.
DOI：

Yu LI, Xue-ying LIU, Hong-qun ZHANG, et al. Optical remote sensing image retrieval based on convolutional neural networks[J]. Optics and precision engineering, 2018, 26(1): 200-207. DOI： 10.3788/OPE.20182601.0200.

摘要

提出了一种基于深度卷积神经网络的光学遥感图像检索方法。首先，通过多层卷积神经网络对遥感图像进行卷积和池化处理，得到每幅图像的特征图，抽取高层特征构建图像特征库；在此过程中使用特征图完成网络模型参数和Softmax分类器的训练。然后，借助Softmax分类器在图像检索阶段对查询图像引入类别反馈，提高图像检索准确度，并根据查询图像特征和图像特征库中特征向量之间的距离，按相似程度由大到小进行排序，得到最终的检索结果。在高分辨率遥感图像数据库中进行了实验，结果显示：针对水体、植被、建筑、农田、裸地等5类图像的平均检索准确度约98.4%，增加飞机、舰船后7类遥感图像的平均检索准确度约95.9%；类别信息的引入有效提高了遥感图像的检索速度和准确度，检索时间减少了约17.6%；与颜色、纹理、词袋模型的对比实验表明，利用深度卷积神经网络抽取的高层信息能够更好地描述图像内容。实验表明该方法能够有效提高光学遥感图像的检索速度和准确度。

Abstract

A method for remote sensing image retrieval based on convolutional neural networks was proposed. First

the convolution and pooling of remote sensing images were conducted by multi-layer convolutional neural networks. The feature maps of each image were obtained

and the high-level features were extracted to build the image feature database. In this process

the training of networks' parameters and the Softmax classifier were completed using feature maps. Then

in the image retrieval stage

classification was introduced by the softmax classifier which will improve the accuracy of image retrieval. Lastly

the remote sensing image retrieval was sorted based on the similarity between the query image and database. Retrieval experiments were performed on the high-resolution optical remote sensing images. The average retrieval precision on five kinds including water

plant

building

farmland and land is 98.4%

and the retrieval precision on seven types (adding plane and ship) is 95.9%. The introduction of class information improves the retrieval precision and speed

saving time by 17.6% approximately. The proposed method behaves better than the methods that based on color feature

texture feature and the bag of words model

and the results show that the high-level feature from deep convolutional neural networks can represent image content effectively. Experimeat indicates that retrieval speed and accuracy of optical remote-sensing images can be effectively increased in this method.

关键词

Keywords

references

PIEDRA-FERNÁNDEZ J A, ORTEGA G, WANG J Z, et al.. Fuzzy content-based image retrieval for oceanic remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9):5422-5431.

ZHU B, RAMSEY M, CHEN H. Creating a large-scale content-based airphoto image digital library[J]. IEEE Transactions on Image Processing, 2000, 9(1):163-167.

MAHESHWARY P, SRIVASTAVA N. Prototype system for retrieval of remote sensing images based on color moment and gray level co-occurrence matrix[J]. International Journal of Computer Science Issues, 2009, 3:20-23.

陆丽珍, 刘仁义, 刘南.一种融合颜色和纹理特征的遥感图像检索方法[J].中国图像图形学报, 2004, 9(3):328-333.

LU L ZH, LIU R Y, LIU N. Remote sensing image retrieval using color and texture fused features[J]. Journal of Image and Graphics, 2004, 9(3):328-333. (in Chinese)

DEMIR B, BRUZZONE L. A novel active learning method in relevance feedback for content-based remote sensing image retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5):2323-2334.

高恒振, 万建伟, 粘勇健, 等.组合核函数支持向量机高光谱图像融合分类[J].光学精密工程, 2011, 19(4):878-883.

GAO H ZH, WAN J W, NIAN Y J, et al.. Fusion classification of hyperspectral image by composite kernels support vector machine[J]. Opt. Precision Eng., 2011, 19(4):878-883. (in Chinese)

GUO Y M, LIU Y, OERLEMANS A, et al.. Deep learning for visual understanding:A review[J]. Neurocomputing, 2016, 187:27-48.

ALZU'BI A, AMIRA A, RAMZAN N. Semantic content-based image retrieval:A comprehensive study[J]. Journal of Visual Communication and Image Representation, 2015, 32:20-54.

LUUS F P S, SALMON B P, VAN DEN BERGH F, et al.. Multiview deep learning for land-use classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12):2448-2452.

熊昌镇, 单艳梅, 郭芬红.结合主体检测的图像检索方法[J].光学精密工程, 2017, 25(3):792-798.

XIONG CH ZH, SHAN Y M, GUO F H. Image retrieval method based on image principal part detection[J]. Opt. Precision Eng., 2017, 25(3):792-798. (in Chinese)

刘海龙, 李宝安, 吕学强, 等. 基于深度卷积神经网络的图像检索算法研究[J/OL]. 计算机应用研究, 2017, 34(12), [2017-01-23]. http://www.arocmag.com/article/02-2017-12-007.html.

LIU H L, LI B A, LYU X Q, et al . . Image retrieval based on deep convolutional neural network[J/OL]. Application Research of Computers , 2017, 34(12), [2017-01-23]. http://www.arocmag.com/article/02-2017-12-007.html http://www.arocmag.com/article/02-2017-12-007.html . (in Chinese)

HUBEL D H, WIESEL T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. The Journal of Physiology, 1962, 160(1):106-154.

FUKUSHIMA K, MIYAKE S, ITO T. Neocognitron:A neural network model for a mechanism of visual pattern recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1983, SMC-13(5):826-834.

LECUN Y, BOTTOU L, BENGIO Y, et al.. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.

罗家祥, 林畅赫, 王加朋, 等.结合深度卷积网络与加速鲁棒特征配准的图像精准定位[J].光学精密工程, 2017, 25(2):469-476.

LUO J X, LIN CH H, WANG J P, et al.. Accurate image locating combining deep convolution network with SURF registering[J]. Opt. Precision Eng., 2017, 25(2):469-476. (in Chinese)

HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al.. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4):212-223.

张洪群, 刘雪莹, 杨森, 等.深度学习的半监督遥感图像检索[J].遥感学报, 2017, 21(3):406-414.

ZHANG H Q, LIU X Y, YANG S, et al.. Retrieval of remote sensing images based on semisupervised deep learning[J]. Journal of Remote Sensing, 2017, 21(3):406-414. (in Chinese)

Views

855

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Co-training generative adversarial networks for semi-supervised classification method

Underwater image enhancement based on multi-branch residual attention network

Cross-modality image matching algorithm based on policy gradient and pseudo-twin network

Shape adaptive feature aggregation network for point cloud classification and segmentation

Real-time super-resolution for infrared dynamic object video based on airborne platform

Related Author

Zhe XU

Jie GENG

Wen JIANG

Zhuo ZHANG

Qing-jie ZENG

CHENG Zhuming

LI Jiaxuan

HUANG San'ao

Related Institution

School of Electronics and Information， Northwestern Polytechnical University

School of Electrical and Information Engineering， Anhui University of Technology， Maanshan

Key Laboratory of Opto-Electronic Information Processing，Chinese Academy of Sciences

Shenyang Institute of Automation， Chinese Academy of Sciences

University of Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰