Seismic image classification based on gravitational self-organizing map

doi:10.6046/gtzyyg.2019.03.13

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Abstract

The recognition and extraction of seismic targets in high resolution post-earthquake images pose great challenge to the traditional image classification method. This paper introduces an object-oriented classification method for high resolution post-earthquake images classification, which integrates fractal texture features into a gravitational self-organizing map (gSOM). The method can be summarized as follows. First of all, the mean shift (MS) segmentation algorithm is adopted for initial segmentation in order to obtain homogeneous geographical objects, and the objects are regarded as the basic classification units in the subsequent process. Secondly, the characteristics of objects are quantified by the adaptive combination of the spectral bands and the fractal second order statistics as the texture information extracted from the original seismic image. Finally, the objects as classification units are clustered under the gSOM. For the purpose of controlling the uncertainty in the classification results, these various clustered results are assembled by the consensus function with the least cost. The qualitative and quantitative experiments on the Wenchuan County seismic images demonstrate the effectiveness and accuracy of the proposed method, which not only maintains the integrity of large damage targets, but also reflects details of the small targets at the same time. Also, the method shows the potential in the new technology for high resolution post-event image classification.

Keywords fractal texture gSOM high resolution remote sensing seismic targets object-oriented classification

TP753

Corresponding Authors: Genyun SUN E-mail: genyunsun@163.com

Issue Date: 30 August 2019

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	Hui HUANG
	Xiongwei ZHENG
	Genyun SUN
	Yanling HAO
	Aizhu ZHANG
	Jun RONG
	Hongzhang MA

Cite this article:

Hui HUANG,Xiongwei ZHENG,Genyun SUN, et al. Seismic image classification based on gravitational self-organizing map[J]. Remote Sensing for Land & Resources, 2019, 31(3): 95-103.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.13 OR https://www.gtzyyg.com/EN/Y2019/V31/I3/95

特征名称	计算公式	说明
均值	$μ FD = 1 MN ∑ i = 1 M ∑ j = 1 N I FD (i, j)$	计算区域的分形维数的均值
方差	$σ FD = 1 MN ∑ i = 1 M ∑ j = 1 N (I FD (i, j) - μ FD) 2$	计算区域的分形维数的方差
对比度(CON)	$CO N FD = ∑ n = 1 L n 2 [∑ i = 1 M ∑ j = 1 N p ~ (i, j)], \| i - j \| = n$	度量局部区域变化,值越小纹理越均匀
相异性(DIS)	$DI S FD = ∑ i = 1 M ∑ j = 1 N p ~ (i, j) i - j$	描述区域内像素与邻近像素的相异性
空隙度(L)	$L FD = 1 MN ∑ i = 0 M ∑ j = 0 N I FD (i, j) 2 (1 MN ∑ i = 0 M ∑ j = 0 N I FD (i, j)) 2 - 1$	计算区域中分形维数的“块度”,空隙度越大表明区域同质性越强
角二阶矩(ASM)	$AS M FD = ∑ i = 1 M ∑ j = 1 N p ~ 2 (i, j) lg [p ~ (i, j)]$ 其中: $p ~ (i, j) = 1 2 π σ I FD exp (- (I FD (i, j) - μ FD) 2 2 σ FD 2)$	度量区域的紊乱性,值越小,表明区域越均匀,其中, $p ~ (i, j)$ 为分形维数图像中坐标为 $(i, j)$ 处像素点属于所在类的归一化概率值
熵(ENT)	$EN T FD = - ∑ i = 1 M ∑ j = 1 N p ~ (i, j) lg [p ~ (i, j)]$	度量区域内像素的随机性,值越大表明纹理越复杂
同质性(HOM)	$HO M FD = ∑ i = 1 M ∑ j = 1 N p ~ (i, j) 1 + i - j p ~ (i, j)$	描述区域内像素分布均匀度
偏斜度(S)	$S FD = E (x - μ FD) σ FD 3$	描述区域内像素分形纹理值的不对称性
逆差距(IDM)	$ID M FD = ∑ i = 1 M ∑ j = 1 N 1 1 + (i - j) 2 p ~ (i, j)$	类似于同质性,但是更强调区域内像素之间的差异性

Tab.1 Formulas and meanings of fractal texture features

Fig.1 General flowchart of the high resolution seismic image classification

Fig.2 Seismic images T1 and T2, MS segmentation results and the corresponding ROI

Fig.3 Comparison of the classification results of T1 and T2

Tab.2 Confusion matrix and accuracy assessment of T1 using SVM

Tab.3 Confusion matrix and accuracy assessment of T1 using the proposed algorithm

Tab.4 Confusion matrix and accuracy assessment of T2 using SVM

Tab.5 Confusion matrix and accuracy assessment of T2 using the proposed algorithm

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