高分辨率遥感影像场景的多尺度神经网络分类法

doi:10.11947/j.AGCS.2018.20170191

测绘学报 ›› 2018, Vol. 47 ›› Issue (5): 620-630.doi: 10.11947/j.AGCS.2018.20170191

高分辨率遥感影像场景的多尺度神经网络分类法

郑卓^1,2, 方芳¹, 刘袁缘¹, 龚希¹, 郭明强¹, 罗忠文¹

1. 中国地质大学(武汉)信息工程学院, 湖北武汉 430074;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2017-04-17 修回日期:2018-02-09 出版日期:2018-05-20 发布日期:2018-06-01
通讯作者: 刘袁缘 E-mail:liuyy@cug.edu.cn
作者简介:郑卓(1996-),男,本科生,研究方向为深度学习,遥感影像解译。
基金资助:
国家自然科学基金（61602429；41701446）；中国地质调查项目（KZ17Z618）

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

ZHENG Zhuo^1,2, FANG Fang¹, LIU Yuanyuan¹, GONG Xi¹, GUO Mingqiang¹, LUO Zhongwen¹

1. College of Information Engineering, China University of Geosciences, Wuhan 430074, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2017-04-17 Revised:2018-02-09 Online:2018-05-20 Published:2018-06-01
Supported by:
The National Natural Science Foundation of China (Nos.61602429;41701446),Chinese Geologic Survey Project (No.KZ17Z618)

摘要/Abstract

摘要： 高分辨率遥感影像场景分类是实现复杂场景快速自动识别的基础，在军事、救灾等领域有十分重要的意义。为了在有限的遥感数据集上获得高识别精度，本文提出了一种基于联合多尺度卷积神经网络模型的高分辨率遥感影像场景分类方法。不同于传统的卷积神经网络模型，JMCNN建立了一个具有3个不同尺度通道的端对端多尺度联合卷积网络模型，包括多通道特征提取器、多尺度特征联合和Softmax分类3个部分。首先，多通道特征提取器提取图像中、高层多尺度特征；然后，多尺度特征联合对多个通道的中、高层多尺度特征进行多次融合以增强特征表达；最后，Softmax对高层特征进行分类。本文在UC Merced和SIRI遥感数据集进行测试，试验表明JMCNN模型在特征表达和计算速度方面均有显著提高，在小样本数据量下分别达到89.3%和88.3%的识别精度。

关键词: 高分辨率遥感影像, 场景分类, 联合多尺度卷积神经网络, 高层特征增强表达, 有限数据集

Abstract: High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN) method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

Key words: high resolution remote sensing imagery, scene classification, joint multi-scale convolution neural network, enhanced high-level feature representation, limit datasets

中图分类号:

P237

郑卓, 方芳, 刘袁缘, 龚希, 郭明强, 罗忠文. 高分辨率遥感影像场景的多尺度神经网络分类法[J]. 测绘学报, 2018, 47(5): 620-630.

ZHENG Zhuo, FANG Fang, LIU Yuanyuan, GONG Xi, GUO Mingqiang, LUO Zhongwen. Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(5): 620-630.

参考文献

[1] CHERIYADAT A M. Unsupervised Feature Learning for Aerial Scene Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):439-451.
[2] SERRANO N, SAVAKIS A E, LUO J B. Improved Scene Classification Using Efficient Low-Level Features and Semantic Cues[J]. Pattern Recognition, 2004, 37(9):1773-1784.
[3] 殷慧, 曹永锋, 孙洪. 基于多维金字塔表达和AdaBoost的高分辨率SAR图像城区场景分类算法[J]. 自动化学报, 2010, 36(8):1099-1106. YIN Hui, CAO Yongfeng, SUN Hong. Urban Scene Classification Based on Multi-dimensional Pyramid Representation and AdaBoost Using High Resolution SAR Images[J]. Acta Automatica Sinica, 2010, 36(8):1099-1106.
[4] LAZEBNIK S, SCHMID C, PONCE J. Beyond Bags of Features:Spatial Pyramid Matching for Recognizing Natural Scene Categories[C]//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY:IEEE, 2006:2169-2178.
[5] YANG Yi, NEWSAM S. Spatial Pyramid Co-occurrence for Image Classification[C]//IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:1465-1472.
[6] ZHAO Bei, ZHONG Yanfei, ZHANG Liangpei. Scene Classification via Latent Dirichlet Allocation Using a Hybrid Generative/Discriminative Strategy for High Spatial Resolution Remote Sensing Imagery[J]. Remote Sensing Letters, 2013, 4(12):1204-1213.
[7] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet Classification with Deep Convolutional Neural Networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada:ACM, 2012:1097-1105.
[8] HECHT-NIELSEN R. Theory of the Backpropagation Neural Network[C]//International Joint Conference on Neural Networks. Washington, DC:IEEE, 1989(1):593-605.
[9] 何小飞, 邹峥嵘, 陶超, 等. 联合显著性和多层卷积神经网络的高分影像场景分类[J]. 测绘学报, 2016, 45(9):1073-1080. DOI:10.11947/j.AGCS.2016.20150612. HE Xiaofei, ZOU Zhengrong, TAO Chao, et al. Combined Saliency with Multi-Convolutional Neural Network for High Resolution Remote Sensing Scene Classification[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1073-1080. DOI:10.11947/j.AGCS.2016.20150612.
[10] CASTELLUCCIO M, POGGI G, SANSONE C, et al. Land Use Classification in Remote Sensing Images by Convolutional Neural Networks[J]. Acta Ecologica Sinica, 2015, 28(2):627-635.
[11] 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]//IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA:IEEE, 2015:44-51.
[12] 李学龙, 史建华, 董永生, 等. 场景图像分类技术综述[J]. 中国科学(信息科学), 2015, 45(7):827-848. LI Xuelong, SHI Jianhua, DONG Yongsheng, et al. A Survey on Scene Image Classification[J]. Scientia Sinica (Informationis), 2015, 45(7):827-848.
[13] LI Haoxiang, LIN Zhe, SHEN Xiaohui, et al. A Convolutional Neural Network Cascade for Face Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:5325-5334.
[14] GLOROT X, BORDES A, BENGIO Y. Deep Sparse Rectifier Neural Networks[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, Florida:HAL, 2011:315-323.
[15] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going Deeper with Convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:1-9.
[16] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[17] ZHAO Bei, ZHONG Yanfei, XIA Guisong, et al. Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):2108-2123.
[18] ZHAO Bei, ZHONG Yanfei, ZHANG Liangpei, et al. The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification[J]. Remote Sensing, 2016, 8(2):157.
[19] ZHU Qiqi, ZHONG Yanfei, ZHAO Bei, et al. Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6):747-751.
[20] ABADI M, BARHAM P, CHEN Jianmin, et al. TensorFlow:A System for Large-scale Machine Learning[C]//Proceedings of the 12th Usenix Conference on Operating Systems Design and Implementation. Berkeley, CA:USENIX Association, 2016.
[21] ZHANG Fan, DU Bo, ZHANG Liangpei. Saliency-Guided Unsupervised Feature Learning for Scene Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4):2175-2184.
[22] LIENOU M, MAITRE H, DATCU M. Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(1):28-32.
[23] CHEN Shizhi, TIAN YingLi. Pyramid of Spatial Relations for Scene-level Land Use Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4):1947-1957.
[24] BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32.
[25] ADANKON M M, CHERIET M. Support Vector Machine[J]. Computer Science, 2002, 1(4):1-28.

高分辨率遥感影像场景的多尺度神经网络分类法

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	白坤, 慕晓冬, 陈雪冰, 朱永清, 尤轩昂. 融合半监督学习的无监督遥感影像场景分类[J]. 测绘学报, 2022, 51(5): 691-702.
[2]	何直蒙, 丁海勇, 安炳琪. 高分辨率遥感影像建筑物提取的空洞卷积E-Unet算法[J]. 测绘学报, 2022, 51(3): 457-467.
[3]	张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2022, 51(1): 135-144.
[4]	施慧慧, 徐雁南, 滕文秀, 王妮. 高分辨率遥感影像深度迁移可变形卷积的场景分类法[J]. 测绘学报, 2021, 50(5): 652-663.
[5]	傅琛, 黄升钶, 汤焱, 吴杭彬, 刘春, 姚连璧, 黄炜. 结合行驶场景语义的轨迹-路网实时匹配方法[J]. 测绘学报, 2021, 50(11): 1617-1627.
[6]	张涛, 丁乐乐, 史芙蓉. 高分辨率遥感影像城中村提取的景观语义指数方法[J]. 测绘学报, 2021, 50(1): 97-104.
[7]	李彦胜, 孔德宇, 张永军, 季铮, 肖锐. 联合稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类[J]. 测绘学报, 2020, 49(12): 1564-1574.
[8]	叶利华, 王磊, 张文文, 李永刚, 王赠凯. 高分辨率光学遥感场景分类的深度度量学习方法[J]. 测绘学报, 2019, 48(6): 698-707.
[9]	左宗成, 张文, 张东映. 融合可变形卷积与条件随机场的遥感影像语义分割方法[J]. 测绘学报, 2019, 48(6): 718-726.
[10]	董志鹏, 王密, 李德仁, 王艳丽, 张致齐. 遥感影像目标的尺度特征卷积神经网络识别法[J]. 测绘学报, 2019, 48(10): 1285-1295.
[11]	范荣双, 陈洋, 徐启恒, 王竞雪. 基于深度学习的高分辨率遥感影像建筑物提取方法[J]. 测绘学报, 2019, 48(1): 34-41.
[12]	董志鹏, 王密, 李德仁, 王艳丽, 张致齐. 利用对象光谱与纹理实现高分辨率遥感影像云检测方法[J]. 测绘学报, 2018, 47(7): 996-1006.
[13]	杨幸彬, 吕京国, 江珊, 张丹璐. 高分辨率遥感影像DSM的改进半全局匹配生成方法[J]. 测绘学报, 2018, 47(10): 1372-1384.
[14]	刘莹, 陶超, 闫培, 邹峥嵘. 图割能量驱动的高分辨率遥感影像震害损毁建筑物检测[J]. 测绘学报, 2017, 46(7): 910-917.
[15]	林祥国, 张继贤. 面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用[J]. 测绘学报, 2017, 46(6): 724-733.