Classification of remote sensing images based on multi-scale feature fusion using local binary patterns

doi:10.6046/zrzyyg.2020303

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(4495 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

For the classification of remote sensing images, traditional feature extraction methods frequently ignore their intrinsic properties and the multi-scale local characteristics of the images. As a result, only a small amount of image information can be acquired. Given this, this study proposed a model of multi-scale gray level and texture feature fusion (Ms_GTSFF ) for the feature extraction of remote sensing images, and the extraction steps are as follows. Firstly, extract the gray-level features of the images at different scales. Then obtain the local texture features of the images using the local binary pattern (LBP) algorithm and meanwhile, obtain the image features of a larger receptive field using a multi-scale method. Afterward, obtain the gray-level attributes corresponding to various codes using the obtained multi-scale LBP histograms. Finally, code and fuse multi-scale feature information obtained from the above steps to constitute the Ms_GTSFF feature extraction model, to which multiple machine learning classifiers are connected for classification and recognition. Taking the aerial hyperspectral remote sensing images of Xiongan New Area (Matiwan Village) as the test dataset, the feature extraction and classification tests were performed following the data preprocessing by blocks. The classification accuracy was up to 99.44%, indicating a great improvement in the recognition capability compared with traditional methods. This verified the effectiveness of the proposed model in enhancing the feature extraction capability and improving the classification and reorganization performance of remote sensing images.

Keywords Hyperspectral remote sensing multi-scale characteristic gray-level attribute feature local binary pattern feature fusion

ZTFLH:

TP79

Issue Date: 24 September 2021

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yanan JIANG
	Xin ZHANG
	Chunlei ZHANG
	Chengcheng ZHONG
	Junfang ZHAO

Cite this article:

Yanan JIANG,Xin ZHANG,Chunlei ZHANG, et al. Classification of remote sensing images based on multi-scale feature fusion using local binary patterns[J]. Remote Sensing for Natural Resources, 2021, 33(3): 36-44.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020303 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/36

Fig.1 Local Binary Pattern feature extraction schematic

Fig.2 Multi-scale LBP feature extraction method

Tab.1 Rotation invariant LBP coding and original LBP coding correspondence table

Fig.3 Model of Ms_GTSFF method

Fig.4 Diagram of the dataset

Fig.5 Histogram of the area of pear by LB $P 8,1 ri 36$ feature extraction

Fig.6 Grayscale image and LB $P 8,1 ri 36$ histogram of 20 types of ground objects under PCA01

Fig.7 Gray-scale feature maps and histogram of MsLB $P 8,1 ri 36$ of images in scales

Tab.2 Comparison of accuracy of classifiers(%)

Fig.8 Comparison of classification results of various scenes in remote sensing images

[1]	滕文秀, 王妮, 陈泰生, 等. 基于深度对抗域适应的高分辨率遥感影像跨域分类[J]. 激光与光电子学进展, 2019, 56(11):236-246.
[1]	Teng W X, Wang N, Chen T S, et al. Deep adversarial domain adaptation method for cross-domain classification in high-resolution remote sensing images[J]. Laser & Optoelectronics Progress, 2019, 56(11):236-246.
[2]	董蕴雅, 张倩. 基于CNN的高分遥感影像深度语义特征提取研究综述[J]. 遥感技术与应用, 2019, 34(1):1-11.
[2]	Dong Y Y, Zhang Q. A survey of depth semantic feature extraction of high-resolution remote sensing images based on CNN[J]. Remote Sensing Technology and Application, 2019, 34(1):1-11.
[3]	Yang Y, Newsam S. Comparing SIFT descriptors and Gabor texture features for classification of remote sensed imagery[C]. 2008 15th IEEE International Conference on Image Processing,San Diego,CA, 2008:1852-1855.doi: 10.1109/ICIP.2008.4712139. doi: 10.1109/ICIP.2008.4712139
[4]	Dos Santos J A, Penatti,O A B, Torres R D S. Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification[C]. VISAPP 2010-Proceedings of the International Conference on Computer Vision Theory and Applications, 2010(2):203-208.
[5]	Chen C, Zhang B, Su H, et al. Land-use scene classification using multi-scale completed local binary patterns[J]. Signal,Image& Video Processing, 2016, 10(4):745-752.
[6]	Luo B, Jiang S J, Zhang L P. Indexing of remote sensing images with different resolutions by multiple features[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(4):1899-1912. doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[7]	Lecun Y, Bottou L. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324. doi: 10.1109/5.726791 url: http://ieeexplore.ieee.org/document/726791/
[8]	Zhong Y F, Fei F, Zhang L P. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery[J]. Journal of Applied Remote Sensing, 2016, 10(2):025006. doi: 10.1117/1.JRS.10.025006 url: http://remotesensing.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JRS.10.025006
[9]	Hu F, Xia G S, Hu J w, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11):14680-14707. doi: 10.3390/rs71114680 url: http://www.mdpi.com/2072-4292/7/11/14680
[10]	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.
[10]	Xu S H, Mu X D, Zhao P, 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.
[11]	Li E, Xia J, Du P, et al. Integrating multilayer features of convolutional neural networks for remote sensing scene classification[J]. IEEE Transactions on Geosience & Remote Sensing, 2017(10):1-13.
[12]	Wang G L, Fan B, Xiang S M, et al. Aggregating rich hierarchical features for scene classification in remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(9):4104-4115. doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[13]	Ojala T, Pietikäinen M, Mäenpää T. Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(7):971-987.doi: 10.1109/TPAMI.2002.1017623 doi: 10.1109/TPAMI.2002.1017623
[14]	Lee S W, Li S Z. Face detection based on multi-block LBP representation[C]// Advances in Biometrics,International Conference,Icb,Seoul,Korea,August.DBLP, 2007:11-18.
[15]	王家臣, 李良晖, 杨胜利. 不同照度下煤矸图像灰度及纹理特征提取的实验研究[J]. 煤炭学报, 2018, 43(11):3051-3061.
[15]	Wang J C, Li L H, Yang S L. Experimental study on gray and texture features extraction of coal and gangue image under different illuminance[J]. Journal of China Coal Society, 2018, 43(11):3051-3061.
[16]	岑奕, 张立福, 张霞, 等. 雄安新区马蹄湾村航空高光谱遥感影像分类数据集[J]. 遥感学报, 2020, 24(11):1299-1306.
[16]	Cen Y, Zhang L F, Zhang X, et al. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village)[J]. Journal of Remote Sensing (Chinese), 2020, 24(11):1299-1306.

[1]	NIU Xianghua, HUANG Wei, HUANG Rui, JIANG Sili. A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion[J]. Remote Sensing for Natural Resources, 2023, 35(3): 116-123.
[2]	ZHENG Zongsheng, LIU Haixia, WANG Zhenhua, LU Peng, SHEN Xukun, TANG Pengfei. Improved 3D-CNN-based method for surface feature classification using hyperspectral images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 105-111.
[3]	KONG Zhuo, YANG Haitao, ZHENG Fengjie, LI Yang, QI Ji, ZHU Qinyu, YANG Zhonglin. Research advances in atmospheric correction of hyperspectral remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(4): 1-10.
[4]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[5]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[6]	ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
[7]	LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[8]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[9]	HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
[10]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[11]	SUN Ke. Remote sensing image classification based on super pixel and peak density[J]. Remote Sensing for Land & Resources, 2020, 32(4): 41-45.
[12]	WANG Ruijun, ZHANG Chunlei, SUN Yongbin, WANG Shen, DONG Shuangfa, WANG Yongjun, YAN Bokun. Application of hyperspectral spectroscopy to constructing polymetallic prospecting model in Hongshan, Gansu Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 222-231.
[13]	Donghui ZHANG, Yingjun ZHAO, Kai QIN. Design and construction of the typical ground target spectral information system[J]. Remote Sensing for Land & Resources, 2018, 30(4): 206-211.
[14]	Jing CUI, Xinfeng DONG, Rui DING, Shimin ZHANG, Conghe WANG, Hengxin LU, Yanyun SUN. Stratigraphic division of loess along loess profile based on hyperspectral remote sensing[J]. Remote Sensing for Land & Resources, 2018, 30(2): 202-207.
[15]	REN Guangli, YANG Min, LI Jianqiang, GAO Ting, LIANG Nan, YI Huan, YANG Junlu. Application of hyperspectral alteration information to gold prospecting: A case study of Fangshankou area,Beishan[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 182-190.

Viewed

Full text

Abstract

Cited

Shared

Discussed