Scale estimation of object-oriented image analysis based on spectral-spatial statistics

Yanni MA; Dongping MING; Haiping YANG

doi:10.11834/jrs.20176374

Views : 0 下载量: 78 CSCD: 21 更多指标

PDF
Export
Share
Collection
Album

Scale estimation of object-oriented image analysis based on spectral-spatial statistics
Vol. 21, Issue 4, Pages: 566-578(2017)
Published： 2017-7 ，

Accepted： 27 February 2017
DOI： 10.11834/jrs.20176374

扫描看全文

马燕妮, 明冬萍, 杨海平. 2017. 面向对象影像多尺度分割最大异质性参数估计. 遥感学报, 21(4): 566–578

Ma Y N, Ming D P and Yang H P. 2017. Scale estimation of object-oriented image analysis based on spectral-spatial statistics. Journal of Remote Sensing, 21(4): 566–578
马燕妮, 明冬萍, 杨海平. 2017. 面向对象影像多尺度分割最大异质性参数估计. 遥感学报, 21(4): 566–578 DOI： 10.11834/jrs.20176374.

Ma Y N, Ming D P and Yang H P. 2017. Scale estimation of object-oriented image analysis based on spectral-spatial statistics. Journal of Remote Sensing, 21(4): 566–578 DOI： 10.11834/jrs.20176374.

摘要

多尺度分割是遥感影像分析的关键步骤，影像分割过程中的尺度参数选择直接关系到面向对象影像分析的质量和精度。首先，总结了面向对象影像分析中尺度概念的内涵，分析遥感影像空间和属性两大基本特征，依据空间统计和光谱统计获得理论上最优的空间尺度分割参数、属性尺度分割参数。其次，运用了基于谱空间统计的高分辨率影像分割尺度估计方法，分析了分形网络演化多尺度分割与影像谱空间统计特征的关系，进而将基于谱空间统计的面向对象影像分析尺度参数应用于分形网络演化多尺度分割算法中，最后，对其参数的合理性进行验证。研究采用高空间分辨率IKONOS和SPOT 5影像数据，选择建筑实验区和农田实验区进行空间和光谱特征统计，以进一步估计分割中的最佳尺度参数。使用分形网络演化方法对图像进行分割，利用监督分类对本文提出的尺度估计方法进行验证，验证结果表明尺度估计方法可以一定程度上保证后续的面向对象影像分类的精度。不同于以往分割后评价的尺度选择方法会需要大量的运算量，本文方法不需要先验知识的参与，且在分割前就可以自适应地估计出相对较为合适的尺度参数，提高了面向对象信息提取的自动化程度。

Abstract

Multi-scale segmentation is the key step of analysis in remotely-sensed imagery. Scale parameter selection in the segmentation process is directly related to the quality and accuracy of object-oriented analysis. Only on the basis of experience for segmentation parameter choice that has less quantitative analysis ways

currently. These methods lack quantitative estimation before segmentation

with considerable workloads and low efficiency. From the perspective of scientific research

the scientificity and universality of these methods are poor. The objective of this paper is to use the quantitative method to determine the scale parameterin order to realize the automatic extracting target on the object-oriented analysis. This paper summarizes the concept of scale parameter in the object-oriented analysis. The commonly used segmentation scale parameters into spatial and attribute bandwidths were also analyzed. This paper used a spatial and spectral statistics-based scale parameter selection method for object-based information extraction from high spatial resolution remote sensing images. The relationship between Fractal Net Evolution Approach (FNEA) in multi-scale segmentation and spatial statistical characteristics was analyzed. Scale estimation based on spatial and spectral statistical characteristics was applied to FNEA in multi-scale segmentation. Meanwhile

the scale estimation approach proposed in this paper was verified by high spatial resolution image

namely IKONOS and SPOT 5 data. Construction and farmland areas were selected for spatial and spectral statistical characteristics

respectively

to further estimate the optimal scale parameters in segmentation. A series of supervised classification was performed to verify the reasonability of the predicted optimal scale parameter. The classification and accuracy assessment results show that the estimated scale by spatial statistical characteristics is basically close to the optimal one in the FNEA-based multi-scale segmentation. The proposed scale estimation approach can ensure the accuracy of the following object-oriented image classification. The method can be used to estimate the appropriate scale parameters before segmentation. In addition

it is an essentially data-driven method that requires nearly no prior knowledge; thus

it can enhance the efficiency and automatic degree of object-based image analysis.

关键词

面向对象影像分析影像分割尺度估计尺度选择空间统计分形网络演化方法

Keywords

object oriented image analysisimage segmentationscale estimationscale selectionspatial statisticsfractal net evolution approach

references

Atkinson P M and Aplin P. 2004. Spatial variation in land cover and choice of spatial resolution for remote sensing. International Journal of Remote Sensing, 25(18): 3687–3702

Baatz M and Schäpe A. 2000.Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation//AngewandteGeographischeInformationsverarbeitung XII: Beiträgezum AGIT-Symposium. Salzburg: Herbert Wichmann Verlag.

Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3/4): 239–258

Blaschke T and Hay G J. 2001. Object-oriented image analysis and scale-space: theory and methods for modeling and evaluating multi-scale landscape structure. International Archives of Photogrammetry and Remote Sensing, 34: 22–29

Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R Q, van der Meer F, van der Werff H, van Coillie F and Tiede D. 2014. Geographic object-based image analysis-towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180–191

陈杰, 邓敏, 肖鹏峰, 杨敏华, 梅小明, 刘慧敏. 2011. 纹理频谱分析的高分辨率遥感影像最佳尺度选择. 遥感学报, 15(3): 492–511

Chen J, Deng M, Xiao P F, Yang M H, Mei X M and Liu H M. 2011. Optimal spatial scale choosing for high resolution imagery based on texture frequency analysis. Journal of Remote Sensing, 15(3): 492–511

Comaniciu D. 2003. An algorithm for data-driven bandwidth selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2): 281–288

Definiens AG.2007. Definiens Developer 7 User Guide, version 7.0.2.936, 23.

Drăgut L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859–871 ])

Dronova I, Gong P, Clinton N E, Wang L, Fu W, Qi S H and Liu Y. 2012. Landscape analysis of wetland plantfunctional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sensing of Environment, 127: 357–369

冯霞, 秦昆, 崔卫红, 陈一祥, 李向辉. 2014. 高分辨率遥感影像目标形状特征多尺度描述与识别. 遥感学报, 18(1): 90–104

Feng X, Qin K, Cui W H, Chen Y X and Li X H. 2014. Multiscale description and recognition of target shape in high-resolution remote sensing images. Journal of Remote Sensing, 18(1): 90–104

Hay G J, Castilla G, Wulder M A and Ruiz J R. 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, 7(4): 339–359 ])

Johnson B and Xie Z X. 2011 2011. Unsupervised image segmentation evaluation and refinement using a multi-scaleapproach. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4): 473–483

Jung C R. 2007. Combining wavelets and watersheds for robust multiscale image segmentation. Image and Vision Computing, 25(1): 24–33

Kim M, Madden M and Warner T.2008. Estimation of optimal image object size for the segmentation of foreststands with multispectral IKONOS imagery//Blaschke T, Lang S and Hay G J, eds. Object-based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin: Springer: 291–307[DOI: 10.1007/978-3-540-77058-9_16]

Ming D P, Ci T Y, Cai H Y, Li L X, Qiao C and Du J Y. 2012. Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geoscience and Remote Sensing Letters, 9(5): 813–817

Ming D P, Li J, Wang J Y and Zhang M. 2015. Scale parameter selection by spatial statistics for GeOBIA: using mean-shift based multi-scale segmentation as an example. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 28–41

明冬萍, 王群, 杨建宇. 2008. 遥感影像空间尺度特性与最佳空间分辨率选择. 遥感学报, 12(4): 529–537

Ming D P, Wang Q and Yang J Y. 2008. Spatial scale of remote sensing image and selection of optimal spatial resolution. Journal of Remote Sensing, 12(4): 529–537

明冬萍, 周文, 汪闽. 2016. 基于谱空间统计特征的高分辨率影像分割尺度估计. 地球信息科学学报, 18(5): 622–631

Ming D P, Zhou W and Wang M. 2016. Scale parameter estimation based on the spatial and spectral statistics in high spatial resolution image segmentation. Journal of Geo-Information Science, 18(5): 622–631

Myint S W, Gober P, Brazel A, Grossman-Clarke S and Weng Q H. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5): 1145–1161 ])

Nogueira K, Penatti O A B and dos Santos J A. 2017. Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification. Pattern Recognition, 61: 539–556

Pinheiro P O, Collobert R and Dollar P. 2015. Learning to segment object candidates//Proceedings of the Advances in Neural Information Processing Systems 28. Montreal, Canada: Neural Information Processing Systems

Ševo I and Avramović A. 2016. Convolutional neural network based automatic object detection on aerial images. IEEE Geoscience and Remote Sensing Letters, 13(5): 740–744

Walter V. 2000. Automatic change detection of GIS databases based on classification of multispectral data. International Archives of Photogrammetry and Remote Sensing, 33: 1138–1145

Walter V. 2004. Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry andRemote Sensing, 58(3/4): 225–238

Woodcock C E and Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311–332

Woodcock C E, Strahler A H and Jupp D L B. 1988. The use of variograms in remote sensing: I. Scene models and simulated images. Remote Sensing of Environment, 25(3): 323–348

Yang J, Li P J and He Y H. 2014. A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 94: 13–24

Yang J, He Y H, Caspersun J and Jones T. 2015. A discrepancy measure for segmentation evaluation from the perspective of object recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 186–192

Zhang X L, Xiao P F, Feng X Z, Feng L and Ye N. 2015. Toward evaluating multiscale segmentations of highspatialresolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3694–3706

Zhang Y J. 1997. Evaluation and comparison of different segmentation algorithms. Pattern Recognition Letters, 18(10): 963–974

Zhao W Z and Du S H. 2016a. Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 155–165

Zhao W Z and Du S H. 2016b. Spectral-spatial feature extraction for hyperspectral image classification: adimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4544–4554

Alert me when the article has been cited

提交

Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM

ECM-based segmentation for multi-look SAR image

Forest change detection based on time series images with statistical properties

Segmentation of hyperspectral remote sensing image using vector C-V model

Data sewing algorithm for parallel segmentation of high-resolution remotely sensed image