Remote sensing image classification based on fusion of temporal features

doi:10.6046/gtzyyg.2017.01.06

Abstract
References
Related Articles
Metrics

Download: PDF(2969 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In order to overcome the shortcomings of the traditional image classification based on spectral and texture, the authors propose an image classification method considering temporal features in this paper. Land use vector map in historical period was used as auxiliary data. The objects were extracted by image segmentation under the constraint of land use vector map. The land cover transition probability which represents temporal feature was calculated by iterative statistic method. The joint probability of object based on temporal feature was built after integrating the land cover transition probability into the traditional maximum posteriori probability. The image classification map was obtained by the maximum posteriori probability theory. The experimental results based on the QuickBird image show that the proposed method can improve the accuracy of the image classification result. Compared with things of the traditional classifier using spectral and texture features, the overall classification accuracy and kappa coefficient of the proposed method are increased by 9.8% and 17.9% respectively.

Keywords angle chain code length variable angle chain code curve description feature extraction port detecting image processing

TP751.1

Issue Date: 23 January 2017

	Service

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

	ZHANG Yongmei
	YANG Fei
	XU Jing

Cite this article:

ZHANG Yongmei,YANG Fei,XU Jing. Remote sensing image classification based on fusion of temporal features[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 36-42.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.06 OR https://www.gtzyyg.com/EN/Y2017/V29/I1/36

[1] 白穆,刘慧平,乔瑜,等.高分辨率遥感图像分类方法在LUCC中的研究进展[J].国土资源遥感,2010,22(1):19-23.doi:10.6046/gtzyyg.2010.01.03. Bai M,Liu H P,Qiao Y,et al.New progress in the classification of high spatial resolution satellite images for LUCC[J].Remote Sensing for Land and Resources,2010,22(1):19-23.doi:10.6046/gtzyyg.2010.01.03.
[2] 杨耘,徐丽,颜佩丽.条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类[J].国土资源遥感,2014,26(4):51-55.doi:10.6046/gtzyyg.2014.04.09. Yang Y,Xu L,Yan P L.Urban land use/cover classification of remote sensing using random forests under the framework of conditional random fields[J].Remote Sensing for Land and Resources,2014,26(4):51-55.doi:10.6046/gtzyyg.2014.04.09.
[3] 杨胜,李敏,彭振国,等.一种新的多波段遥感影像变化检测方法[J].中国图像图形学报,2009,14(4):572-578. Yang S,Li M,Peng Z G,et al.A novel multi-band remote sensing image change detection algorithm[J].Journal of Image and Graphics,2009,14(4):572-578.
[4] 万幼川,宋杨.基于高分辨率遥感影像分类的地图更新方法[J].武汉大学学报:信息科学版,2005,30(2):105-109. Wang Y C,Song Y.Application of maps revision based on classification of high resolution satellite images[J].Geomatics and Information Science of Wuhan University,2005,30(2):105-109.
[5] 徐涵秋.基于SFIM算法的融合影像分类研究[J].武汉大学学报:信息科学版,2004,29(10):920-923. Xu H Q.Classification of fused imagery base on the SFIM algorithm[J].Geomatics and Information Science of Wuhan University,2004,29(10):920-923.
[6] 崔炳德.支持向量机分类器遥感图像分类研究[J].计算机工程与应用,2011,47(27):189-191. Cui B D.Remote sensing image classification based on SVM classifier[J].Computer Engineering and Applications,2011,47(27):189-191.
[7] 陈秋晓,骆剑承,周成虎,等.基于多特征的遥感影像分类方法[J].遥感学报,2004,8(3):239-245. Chen Q X,Luo J C,Zhou C H,et al.Classification of remotely sensed imagery using multi-features based approach[J].Journal of Remote Sensing,2004,8(3):239-245.
[8] Zhao Y D,Zhang L P,Li P X,et al.Classification of high spatial resolution imagery using improved Gaussian Markov random-field-based texture features[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(4):1458-1468.
[9] 张楼香,阮仁宗,夏双.洪泽湖湿地纹理特征参数分析[J].国土资源遥感,2015,27(1):75-80.doi:10.6046/gtzyyg.2015.01.12. Zhang L X,Ruan R Z,Xia S.Parameter analysis of image texture of wetland in the Hongze lake[J].Remote Sensing for Land and Resources,2015,27(1):75-80.doi:10.6046/gtzyyg.2015.01.12.
[10] 谭熊,余旭初,张鹏强,等.基于MKSVM和MRF的高光谱影像分类方法[J].国土资源遥感,2015,27(3):42-46.doi:10.6046/gtzyyg.2015.03.08. Tan X,Yu X C,Zhang P Q,et al.Hyperspectral images classification based on MKSVM and MRF[J].Remote Sensing for Land and Resources,2015,27(3):42-46.doi:10.6046/gtzyyg.2015.03.08.
[11] 乔程,沈占锋,吴宁,等.空间邻接支持下的遥感影像分类[J].遥感学报,2011,15(1):88-99. Qiao C,Shen Z F,Wu N,et al.Remote sensing image classification method supported by spatial adjacency[J].Journal of Remote Sensing,2011,15(1):88-99.
[12] 赵红蕊,阎广建,邓小炼,等.一种简单加入空间关系的实用图像分类方法[J].遥感学报,2003,7(5):358-363. Zhao H R,Yan G J,Deng X L,et al.A classification method based on spatial information[J].Journal of Remote Sensing,2003,7(5):358-363.
[13] Bruzzone L,Serpico S B.An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):858-867.
[14] Demir B,Bovolo F,Bruzzone L.Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1930-1941.
[15] 李雪,舒宁,王琰,等.利用土地利用状态转移分析的变化检测[J].武汉大学学报:信息科学版,2011,36(8):952-955. Li X,Shu N,Wang Y,et al.Change detection based on land-use status transition analysis[J].Geomatics and Information Science of Wuhan University,2011,36(8):952-955.
[16] 邓媛媛,巫兆聪,易俐娜,等.面向对象的高分辨率影像农用地分类[J].国土资源遥感,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24. Deng Y Y,Wu Z C,Yi L N,et al.Research on object-oriented classification of agricultural land based on high resolution images[J].Remote Sensing for Land and Resources,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24.
[17] 陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报:信息科学版,2006,31(4):316-320. Chen Y H,Feng T,Shi P J,et al.Classification of remot sensing image based on object oriented and class rules[J].Geomatics and Information Science of Wuhan University,2006,31(4):316-320.
[18] Byun Y G,Han Y K,Chae T B.A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery[J].KSCE Journal of Civil Engineering,2013,17(2):486-497.
[19] Tang Y Q,Zhang L P,Huang X.Object-oriented change detection based on the Kolmogorov-Smirnov test using high-resolution multispectral imagery[J].International Journal of Remote Sensing,2011,32(20):5719-5740.

[1]	QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[2]	CAI Xiang, LI Qi, LUO Yan, QI Jiandong. Surface features extraction of mining area image based on object-oriented and deep-learning method[J]. Remote Sensing for Land & Resources, 2021, 33(1): 63-71.
[3]	WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
[4]	LI Xusheng, LIU Yufeng, CHEN Donghua, LIU Saisai, LI Hu. Cloud detection based on support vector machine with image features for GF-1 data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 55-62.
[5]	Xinghang ZHANG, Lin ZHU, Wei WANG, Lishan MENG, Xiaojuan LI, Yingchao REN. Study and application of sequential extraction method of ground fissures based on object[J]. Remote Sensing for Land & Resources, 2019, 31(1): 87-94.
[6]	WEI Yingjuan, ZHENG Xiongwei, LEI Bing, GAN Yuhang. Realization of clouds automatic extraction of GF-1 remote sensing image based on sample model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 39-45.
[7]	JINAG Wei, HE Guojin, LIU Huichan, LONG Tengfei, WANG Wei, ZHENG Shouzhu, MA Xiaoxiao. Research on China’s land image mosaicking and mapping technology based on GF-1 satellite WFV data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 190-196.
[8]	ZHANG Yongmei, YANG Fei, XU Jing. An improved length variable angle chain code algorithm and its application to dock identification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 164-169.
[9]	LI Fuyu, YE Famao. Summarization of SIFT-based remote sensing image registration techniques[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 14-20.
[10]	JIN Dingjian, ZHI Xiaodong, WANG Jianchao, ZHANG Dandan, SHANG Boxuan. Comparison of UAV remote sensing image processing software for geological disasters monitoring[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 183-189.
[11]	LIU Pengcheng. Feature extraction of coastline contour based on level set theory[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 75-79.
[12]	CHANG Ruichun, WANG Lu, WANG Maozhi. Application of FastICA in mineral information extraction from hyperspectral remote sensing data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 129-132.
[13]	CHEN Yiguo. Crack detection above buildings based on the infrared thermal imaging technology[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(2): 53-56.
[14]	YU Xue-qin, ZUO Xiao-qing, HUANG Liang. A Building Segmentation Algorithm Based on Mathematical Morphology and Multi-scale Segmentation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 41-44.
[15]	LI Ting, ZHAN Qing-ming, YU Liang. A Classification Method for Mobile Laser Scanning Data Based on Object Feature Extraction[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(1): 17-21.

Viewed

Full text

Abstract

Cited

Shared

Discussed