联合多尺度多特征的高分遥感图像场景分类

doi:10.3969/j.issn.0372-2112.2020.09.021

电子学报 ›› 2020, Vol. 48 ›› Issue (9): 1824-1833.DOI: 10.3969/j.issn.0372-2112.2020.09.021

联合多尺度多特征的高分遥感图像场景分类

黄鸿, 徐科杰, 石光耀

重庆大学光电技术及系统教育部重点实验室, 重庆 400044

收稿日期:2019-03-26 修回日期:2020-04-19 出版日期:2020-09-25
- 通讯作者:
- 黄鸿
- 作者简介:
- 徐科杰 男,1994年生于浙江舟山.2017年于浙江师范大学获得学士学位,现为重庆大学博士研究生.主要从事模式识别,图像处理,遥感影像分类等方面的研究.E-mail:xukejie@cqu.edu.cn
  石光耀 男,1988年生于河南项城.2015、2017年于重庆大学分别获得学士和硕士学位,现为重庆大学博士研究生.主要从事图像处理、遥感影像分类、机器视觉与目标追踪等方面的研究.E-mail:shiguangyao@cqu.edu.cn
- 基金资助:
- 重庆市基础与前沿研究计划 (No.cstc2018jcyjAX0093）; 重庆市留学人员回国创业创新支持计划 (No.cx2019144）; 重庆市研究生科研创新项目 (No.CYB19039）; 重庆市教委科学技术研究计划 (No.KJZD-K201902501）

Scene Classification of High-Resolution Remote Sensing Image by Multi-scale and Multi-feature Fusion

HUANG Hong, XU Ke-jie, SHI Guang-yao

Key Laboratory of Optoelectronic Technique System of the Ministry of Education, Chongqing University, Chongqing 400044, China

Received:2019-03-26 Revised:2020-04-19 Online:2020-09-25 Published:2020-09-25
- Corresponding author:
- HUANG Hong
- Supported by:
- Chongqing Research Program of Basic and Frontier Technology (No.cstc2018jcyjAX0093); Venture & Innovation Support Program for Chongqing Overseas Returnees (No.cx2019144); Chongqing Postgraduate Research and Innovation Project (No.CYB19039); Science and Technology Research Project of Chongqing Municipal Education Commission (No.KJZD-K201902501)

摘要/Abstract

摘要： 高分辨率遥感图像地物信息丰富，但场景构成复杂，目前基于手工设计的特征提取方法不能满足复杂场景分类的需求，而非监督特征学习方法尽管能够挖掘局部图像块的本征结构，但单一种类及尺度的特征难以有效表达实际应用中复杂遥感场景特性，导致分类性能受限.针对此问题，本文提出了一种基于多尺度多特征的遥感场景分类方法.该算法首先设计了一种改进的谱聚类非监督特征（iUFL-SC）以有效表征图像块的本征结构，然后通过密集采样提取每幅遥感场景的iUFL-SC、LBP、SIFT等三种多尺度局部图像块特征，并通过视觉词袋模型（BoVW）获得场景的中层特征表达，以实现更为准确详实的特征描述，最后基于直方图交叉核的支持向量机（HIKSVM）进行分类.在UC Merced数据集以及WHU-RS19数据集上的实验结果表明本文方法可对遥感场景进行鉴别特征提取，有效提高分类性能.

关键词: 遥感, 高分辨率影像, 场景分类, 非监督特征, 特征融合, 视觉词袋模型

Abstract: High resolution image possesses abundant information of ground objects. The hand-crafted features cannot meet the demand of complex scene classification due to complex scene distribution, while the unsupervised feature learning method can exploit the intrinsic structure of image patches to obtain effective discriminating features. However, single feature with a scale is difficult to represent the characteristics of complex scenes in practical applications, which restricts classification performance. To solve this problem, this paper proposed a new method based on multi-scale and multi-feature fusion (MMF) for remote sensing scene classification. At first, an improved unsupervised feature learning via spectral clustering (iUFL-SC) is designed to effectively reveal the intrinsic structure of image patches, and then the iUFL-SC, LBP, and SIFT features of image patches are extracted by dense sampling in each image. After that, the middle-level features of each scene are obtained through bag of visual words (BoVW) model for effective feature description. Finally, the fused features are classified by histogram intersection kernel SVM. Experimental results on two public data sets indicate that MMF can extract discriminant features of remote sensing image and subsequently improve the classification performance.

Key words: remote sensing, high resolution images, scene classification, unsupervised features, feature fusion, bag of visual words

中图分类号:

TP751.1

黄鸿, 徐科杰, 石光耀. 联合多尺度多特征的高分遥感图像场景分类[J]. 电子学报, 2020, 48(9): 1824-1833.

HUANG Hong, XU Ke-jie, SHI Guang-yao . Scene Classification of High-Resolution Remote Sensing Image by Multi-scale and Multi-feature Fusion[J]. Acta Electronica Sinica, 2020, 48(9): 1824-1833.

参考文献

[1] 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. HE Xiao-fei,ZOU Zheng-rong,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.(in Chinese)
[2] YANG Y,NEWSAM S.Bag-of-visual-words and spatial extensions for land-use classification[A].Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems[C].New York:ACM,2010.270-279.
[3] 杨赛,赵春霞,刘凡.多核学习融合局部和全局特征的人脸识别算法[J].电子学报,2016,44(10):2344-2350. YANG Sai,ZHAO Chun-xia,LIU Fan.Fusion of local and global features using multiple kernel learning for face recognition[J].Acta Electronica Sinica,2016,44(10):2344-2350.(in Chinese)
[4] 张峰,钟宝江.基于兴趣目标的图像检索[J].电子学报,2018,46(8):1915-1923. ZHANG Feng,ZHONG Bao-jiang.Image retrieval based on interested objects[J].Acta Electronica Sinica,2018,46(8):1915-1923.(in Chinese)
[5] 钱晓亮,李佳,程塨,等.特征提取策略对高分辨率遥感图像场景分类性能影响的评估[J].遥感学报,2018,22(5):758-776. QIAN Xiao-liang,LI Jia,CHENG Gong,et al.Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification[J].Journal of Remote Sensing,2018,22(5):758-776.(in Chinese)
[6] HU J W,XIA G S,HU F,ZHANF L P.A comparative study of sampling analysis in the scene classification of optical high-spatial resolution remote sensing imagery[J].Remote Sensing,2015,7(11):14988-15013.
[7] 胡运强,曹云峰,丁萌,等.基于分层搜索与局部约束线性编码的机场检测[J].光学学报,2018,38(8):243-251. HU Yun-qiang,CAO Yun-feng,DING Meng,et al.Airport detection based on a hierarchical architecture and locality-constrained linear coding[J].Acta Optica Sinica,2018,38(8):243-251.(in Chinese)
[8] CHEN C,ZHANG B C,SU H J,et al.Land-use scene classification using multi-scale completed local binary patterns[J].Signal,Image and Video Processing,2016,10(4):745-752.
[9] 柯圣财,赵永威,李弼程,等.基于卷积神经网络和监督核哈希的图像检索方法[J].电子学报,2017,45(1):157-163. KE Sheng-cai,ZHAO Yong-wei,LI Bi-cheng,et al.Image retrieval based on convolutional neural network and kernel-based supervised hashing[J].Acta Electronica Sinica,2017,45(1):157-163.(in Chinese)
[10] DU B,XIONG W,WU J,et al.Stacked convolutional denoising auto-encoders for feature representation[J].IEEE Transactions on Cybernetics,2017,47(4):1017-1027.
[11] XU Y H,ZHANG L P,DU B,et al.Spectral-spatial unified networks for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(10):5893-5909.
[12] XU Y H,DU B,ZHANG F,et al.Hyperspectral image classification via a random patches network[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,142:344-357.
[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?[A].Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops[C].Boston:IEEE,2015.44-51.
[14] Cheriyadat A M.Unsupervised feature learning for aerial scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):439-451.
[15] ZHANG F,DU B,ZHANG L P,et al.Saliency-guided unsupervised feature learning for scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2175-2184.
[16] HU F,XIA G S,WANG Z F,et al.Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(5):2015-2030.
[17] 何芳,王榕,于强,等.加权空谱局部保持投影的高光谱图像特征提取[J].光学精密工程,2017,25(1):263-273. HE Fang,WANG Rong,YU Qiang,et al.Feature extraction of hyperspectral images of weighted spatial and spectral locality preserving projection[J].Optics and Precision Engineering,2017,25(1):263-273.(in Chinese)
[18] 汪宇玲,黎明,李军华,等.基于BoF模型的多特征融合纹理图像分类[J].北京航空航天大学学报,2018,44(9):1869-1877. WANG Yu-ling,LI Ming,LI Jun-hua,et al.Texture image classification based on BoF model with multi-feature fusion[J].Journal of Beijing University of Aeronautics and Astronautics,2018,44(9):1869-1877.(in Chinese)
[19] YANG Y,NEWSAM S.Spatial pyramid co-occurrence for image classification[A].Proceedings of the 2011 IEEE International Conference on Computer Vision[C].Barcelona:IEEE,2011.1465-1472.
[20] XIA G S,HU J W,HU F,et al.AID:A benchmark data set for performance evaluation of aerial scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(7):3965-3981.
[21] ZHONG Y F,ZHU Q Q,ZHANG L P.Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(11):6207-6222.
[22] SHAO W,YANG W,XIA G S,et al.A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization[A].Proceedings of the 9th International Conference on Computer Vision Systems[C].St Petersburg:Springer,2013.324-333.
[23] 许夙晖,慕晓冬,赵鹏,等.利用多尺度特征与深度网络对遥感影像进行场景分类[J].测绘学报,2016,45(7):834-840. XU Su-hui,MU Xiao-dong,ZHAO Peng,et al.Scene classification of remote sensing image based on multi-scale feature and deep neural network[J].Acta Geodaetica et Cartographica Sinica,2016,45(7):834-840.(in Chinese)
[24] SHENG Y X,BIAN X Y.A novel spatial approach for classification of high-resolution image scene[A].Proceedings of the 12th World Congress on Intelligent Control and Automation[C].Guilin:IEEE,2016.1686-1691.
[25] FERNANDO B,FROMONT E,MUSELET D,et al.Discriminative feature fusion for image classification[A].Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition[C].Providence:IEEE,2012.3434-3441.
[26] HUANG L H,CHEN C,LI W,et al.Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors[J].Remote Sensing,2016,8(6):483.

联合多尺度多特征的高分遥感图像场景分类

Scene Classification of High-Resolution Remote Sensing Image by Multi-scale and Multi-feature Fusion

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

[1]	王硕, 王坚, 王亚男, 宋亚飞. 一种基于特征融合的恶意代码快速检测方法[J]. 电子学报, 2023, 51(1): 57-66.
[2]	琚长瑞, 秦晓燕, 袁广林, 李豪, 朱虹. 尺度敏感损失与特征融合的快速小目标检测方法[J]. 电子学报, 2022, 50(9): 2119-2126.
[3]	王飞扬, 冀鹏欣, 孙笠, 危倩, 李根, 张忠宝. 一种基于深度学习的动态社交网络用户对齐方法[J]. 电子学报, 2022, 50(8): 1925-1936.
[4]	李超, 黄新宇, 王凯. 基于特征融合和自学习锚框的高分辨率图像小目标检测算法[J]. 电子学报, 2022, 50(7): 1684-1695.
[5]	彭闯, 王伦文, 胡炜林. 融合深度特征的电磁频谱异常检测算法[J]. 电子学报, 2022, 50(6): 1359-1369.
[6]	杨曦, 张鑫, 郭浩远, 王楠楠, 高新波. 基于不变特征的多源遥感图像舰船目标检测算法[J]. 电子学报, 2022, 50(4): 887-899.
[7]	肖进胜, 张舒豪, 陈云华, 王元方, 杨力衡. 双向特征融合与特征选择的遥感影像目标检测[J]. 电子学报, 2022, 50(2): 267-272.
[8]	杨晓城, 杨真乙, 阎敬业, 武林, 蒋明峰. 基于拉普拉斯算子的综合孔径辐射计成像算法[J]. 电子学报, 2022, 50(2): 339-345.
[9]	赵旭东, 陶然, 李伟, 张蒙蒙. 高光谱图像分数域信息提取理论与方法进展[J]. 电子学报, 2022, 50(12): 2874-2883.
[10]	魏博文, 全红艳. 基于语义与形态特征融合的语义分割网络[J]. 电子学报, 2022, 50(11): 2688-2697.
[11]	杨创艳, 马军, 王晓东, 罗亭, 李卓睿. 特征融合与灰色回归的滚动轴承性能退化评估[J]. 电子学报, 2022, 50(1): 106-115.
[12]	赵琰, 赵凌君, 匡纲要. 基于注意力机制特征融合网络的SAR图像飞机目标快速检测[J]. 电子学报, 2021, 49(9): 1665-1674.
[13]	郑海颖, 王峰, 姜维, 王志强, 姚西文. 神经网络训练策略对高分辨率遥感图像场景分类性能影响的评估[J]. 电子学报, 2021, 49(8): 1599-1614.
[14]	席亮, 刘涵, 樊好义, 张凤斌. 基于深度对抗学习潜在表示分布的异常检测模型[J]. 电子学报, 2021, 49(7): 1257-1265.
[15]	周勇, 陈思霖, 赵佳琦, 张迪, 王瀚正. 基于弱语义注意力的遥感图像可解释目标检测[J]. 电子学报, 2021, 49(4): 679-689.