Automatic sampling of remote sensing image change detection samples based on prior information of vector data

Dongsheng WEI; Xiaoguang ZHOU

doi:10.11834/jrs.20198013

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Automatic sampling of remote sensing image change detection samples based on prior information of vector data
Vol. 23, Issue 3, Pages: 464-475(2019)
Published： 2019-5 ，

Accepted： 23 April 2018
DOI： 10.11834/jrs.20198013

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魏东升, 周晓光. 2019. 遥感影像变化检测样本自动抽样. 遥感学报, 23(3): 464–475

Wei D S and Zhou X G. 2019. Automatic sampling of remote sensing image change detection samples based on prior information of vector data. Journal of Remote Sensing, 23(3): 464–475
魏东升, 周晓光. 2019. 遥感影像变化检测样本自动抽样. 遥感学报, 23(3): 464–475 DOI： 10.11834/jrs.20198013.

Wei D S and Zhou X G. 2019. Automatic sampling of remote sensing image change detection samples based on prior information of vector data. Journal of Remote Sensing, 23(3): 464–475 DOI： 10.11834/jrs.20198013.

摘要

在遥感影像结合矢量数据先验信息的变化检测中，需要从分割后的影像对象中抽取一定数量、具有相同类别属性的样本，其中不可避免地抽到类别属性不一致的样本，如何剔除这些样本是抽样过程中必须解决的重点问题，在目前已有的方法中，一般是通过人工目视判别完成的。样本的自动提取是实现自动变化检测的关键环节，本文提出一种变化检测样本自动抽样方法，主要包括样本的空间布设和异常样本自动检测两个环节。该方法首先利用矢量数据提取抽样图层，用抽样图层分割遥感影像，获取影像对象。其次是根据抽样区域范围、影像对象分布特征和地形特征布设变化检测样本。然后根据样本的先验类别属性构建特征空间向量，计算样本在特征空间的局部可达密度，由局部可达密度计算样本的异常度指数，并根据特征空间密度异常指数剔除异常样本，完成变化检测样本自动提取。最后以耕地、林地和居民地为例进行了抽样试验。结果表明，邻域参数

按样本布设总数的1/5—1/3取值、异常度阈值设定为80%时，可以实现异常样本0漏检率，能够准确、高效实现变化检测样本的自动提取。

Abstract

Some existing classification vector datasets are available for change detection in many regions

and some prior knowledge—such as position

shape

size

and class—are included in these datasets. The change detection method

which rely on remote sensing images and vector datasets

has become an important research focus. The automatic sampling method for the change detection of samples is the key technology in achieving automatic change detection. Therefore

an automatic sampling method based on the feature space density outlier index is proposed. The change detection of vector data/remote sensing images is realized by focusing on the spectral feature difference between the image objects needed to be detected and the samples. On the one hand

the samples must be able to differentiate the regional environment characteristics in a spatial distribution

and this aspect belongs to the problem realm of the sampling distribution method. On the other hand

the samples must include the image objects with no changes in the posterior class attribute

and this aspect is important in the detection of outlier samples. The automatic sampling method proposed in this study includes the spatial layout of samples and the automatic detection of outlier samples. The reasonable spatial distribution of samples is the precondition for improving the accuracy of change detection results

and an automatic detection method for the outlier samples is an important part of automatic change detection technology. In acquiring the samples for change detection

we initially extract a sampling layer from the vector data and use this layer to segment the remote sensing image. In this manner

the image objects can be extracted. Second

samples are extracted from the image objects

and we determine whether the spatial distribution of these samples directly yet reasonably affects the accuracy of the change detection results. On the one hand

the rationality of the sample space layout should reflect the correctness and uniqueness of the samples in typical normal areas. On the other hand

the sample layout must be able to reflect the overall features of the image objects. The sample layout must also be random and uniform

and the samples should be representative. Thus

we extract samples of the image objects in accordance with the sampling region and by considering the distribution and topographic features of these image objects. Then

we detect the outlier image objects in the samples. Local reachability density (LRD) is applied to quantitatively describe the intensity of an image object relative to its neighborhood’s image objects in the feature space. In particular

the LRD algorithm is used to construct an outlier index that can measure the outlier degree of the sample image objects in the feature space. Feature space vectors are also constructed on the basis of the texture feature parameters. Then

the LRDs of the samples are calculated according to reachability distance. The feature space outlier index (FSOI) is also defined and then used to detect the outlier sample objects. Finally

an experimental research on the proposed sampling method was carried out by using a vector topographic map and a high-resolution remote sensing image. The experimental results indicate that the full detection of outlier samples can be achieved when the

value is set from 1/5 to 1/3 of the total number of samples and the FSOI threshold is set to 80%. The proposed sampling method can effectively achieve automatic sampling of change detection samples.

关键词

遥感影像矢量数据抽样变化检测异常

Keywords

remote sensing imagevector datasamplingchange detectionoutlier

references

Adesina G O and Mavomi I. 2014. Landuse and landcover change detection of Jebba Lake Basin Nigeria: remote sensing and GIS approach. Journal of Environment and Earth Science, 4(5): 119–127

Aggarwal C C and Yu P S. 2001. Outlier detection for high dimensional data. ACM SIGMOD Record, 30(2): 37–46

Ahmad F. 2012. A review of remote sensing data change detection: comparison of Faisalabad and Multan Districts, Punjab Province, Pakistan. Journal of Geography and Regional Planning, 5(9): 236–251

Atkinson A C. 1986. [Influential observations, high leverage points, and outliers in linear regression]: comment: aspects of diagnostic regression analysis. Statistical Science, 1(3): 397–402

Barnhart T B and Crosby B T. 2013. Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, Selawik River, Alaska. Remote Sensing, 5(6): 2813–2837

Breunig M M, Kriegel H P, Ng R T and Sander J. 2000. LOF: identifying density-based local outliers. ACM SIGMOD Record, 29(2): 93–104

Bruzzone L and Prieto D F. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171–1182

Chen C F, Son N T, Chang N B, Chen C R, Chang L Y, Valdez M, Centeno G, Thompson C A and Aceituno L A. 2013. Multi-decadal mangrove forest change detection and prediction in Honduras, central America, with Landsat imagery and a Markov chain model. Remote Sensing, 5(12): 6408–6426

Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H and Mills J. 2015. Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7–27

Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317–321

Chen X H, Chen J, Shi Y S and Yamaguchi Y. 2012. An automated approach for updating land cover maps based on integrated change detection and classification methods. ISPRS Journal of Photogrammetry and Remote Sensing, 71: 86–95

Eichkitz C G, Amtmann J and Schreilechner M G. 2013. Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions. Computers and Geosciences, 60: 176–183

Ghofrani Z, Mokhtarzade M, Reza Sahebi M and Beykikhoshk A. 2014. Evaluating coverage changes in national parks using a hybrid change detection algorithm and remote sensing. Journal of Applied Remote Sensing, 8(1): 083646

Haralick R M, Shanmugam K and Dinstein I H. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6): 610–621

Hawkins D M. 1980. Identification of outliers. Biometrics, 37(4): 860

金晶, 邹峥嵘, 陶超. 2014. 高分辨率遥感影像的压缩纹理元分类. 测绘学报, 43(5): 493–499

Jin J, Zou Z R and Tao C. 2014. Compressed texton based high resolution remote sensing image classification. Acta Geodaetica et Cartographica Sinica, 43(5): 493–499 (

Johnson R D and Kasischke E S. 1998. Change vector analysis: a technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing, 19(3): 411–426

李德仁. 2003. 利用遥感影像进行变化检测. 武汉大学学报(信息科学版), 28(S1): 7–12

Li D R. 2003. Change detection from remote sensing images. Geomatics and Information Science of Wuhan University, 28(S1): 7–12 (

李亮, 舒宁, 龚龑. 2013. 考虑时空关系的遥感影像变化检测和变化类型识别. 武汉大学学报(信息科学版), 38(5): 533–537

Li L, Shu N and Gong Y. 2013. Remote sensing image change detection and change type recognition based on spatiotemporal relationship. Geomatics and Information Science of Wuhan University, 38(5): 533–537 (

Radovanović M, Nanopoulos A and Ivanović M. 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Transactions on Knowledge and Data Engineering, 27(5): 1369–1382

Ramaswamy S, Rastogi R and Shim K. 2000. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2): 427–438

Samadzadegan F and Rastiveisi H. 2008. Automatic detection and classification of damaged buildings, using high resolution satellite imagery and vector data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B8. Beijing, 37: 415–420

Serra P, Pons X and Saurí D. 2003. Post-classification change detection with data from different sensors: some accuracy considerations. International Journal of Remote Sensing, 24(23): 4975–4976

沈小乐, 邵振峰, 田英洁. 2014. 纹理特征与视觉注意相结合的建筑区提取. 测绘学报, 43(8): 842–847

Shen X L, Shao Z F and Tian Y J. 2014. Built-up areas extraction of high-resolution remote sensing images by texture driven visual attention mechanism. Acta Geodaetica et Cartographica Sinica, 43(8): 842–847 (

Sofina N and Ehlers M. 2016. Building change detection using high resolution remotely sensed data and GIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8): 3430–3438

Ulaby F T, Kouyate F, Brisco B and Lee Williams T H. 1986. Textural infornation in SAR images. IEEE Transactions on Geoscience and Remote Sensing, GE-24(2): 235–245

Varma S, Shah V, Banerjee B and Buddhiraju K M. 2014. Change detection of desert sand dunes: a remote sensing approach. Advances in Remote Sensing, 3: 10–22

魏东升, 周晓光. 2017. 顾及纹理特征贡献度的变化影像对象提取算法. 测绘学报, 46(5): 605–613

Wei D S and Zhou X G. 2017. Changed image objects extraction algorithms considering texture feature contribution. Acta Geodaetica et Cartographica Sinica, 46(5): 605–613 (

虞欣. 2009. 贝叶斯网络在航空影像纹理分类中的应用研究. 测绘学报, 38(4): 375

Yu X. 2009. A study of texture classification of aerial image using Bayesian networks. Acta Geodaetica et Cartographica Sinica, 38(4): 375 (

张晓东, 李德仁, 龚健雅, 秦前清. 2006. 遥感影像与GIS分析相结合的变化检测方法. 武汉大学学报(信息科学版), 31(3): 266–269

Zhang X D, Li D R, Gong J Y and Qin Q Q. 2006. A change detection method of integrating remote sensing and GIS. Geomatics and Information Science of Wuhan University, 31(3): 266–269 (

庄会富, 邓喀中, 范洪冬. 2016. 纹理特征向量与最大化熵法相结合的SAR影像非监督变化检测. 测绘学报, 45(3): 339–346

Zhuang H F, Deng K Z and Fan H D. 2016. SAR images unsupervised change detection based on combination of texture feature vector with maximum entropy principle. Acta Geodaetica et Cartographica Sinica, 45(3): 339–346 (

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