Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade

Journal Pre-proofs Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade Junlong Xu, Xingping Wen, Haonan Zhang, Dayou Luo, Jinbo Li, Lianglong Xu, Min Yu PII: DOI: Reference:

S0273-1177(19)30723-9 https://doi.org/10.1016/j.asr.2019.09.045 JASR 14470

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Advances in Space Research

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2 February 2019 9 August 2019 25 September 2019

Please cite this article as: Xu, J., Wen, X., Zhang, H., Luo, D., Li, J., Xu, L., Yu, M., Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade, Advances in Space Research (2019), doi: https://doi.org/10.1016/j.asr.2019.09.045

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Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade Junlong Xua,b, Xingping Wena,b,* , Haonan Zhanga,b, Dayou Luoa,b, Jinbo Lia,b, Lianglong Xua,b, Min Yuc a Faculty b Mineral

of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China; Resources Prediction and Evaluation Engineering Laboratory of Yunan Province, Kunming 650093, China; c School

of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

Abstract: Lineaments refer to the linear or curvilinear textures on remote sensing image, whose general spatial distribution characteristics are often the response of deep crustal structures at the surface. Firstly, we use wavelet modulus maxima transformation to detect the edges with 4 scale on Landsat - 8 OLI B5 image and analyze their multi-scale characteristics. As the result, it is determined that the optimal scale of edge detection is 4, and the outline that consist of the edge pixels is roughly corresponding to the geological structure of mine area. Thus the incomplete lineaments have been extracted by using the 2D otsu algorithm. Secondly, the hillshade map generated based on DEM is processed to generate binarized linear shadow. Finally, the linear shadow is superimposed on the lineaments preliminarily extracted to obtain the optimized lineaments. Experiment results show that, based on the method, there will be some deformation and displacement between the lineaments extracted and the actual geological structure, and it fail to effectively extract the Qilinchang Fault, but lineaments are in good correspondence with Kuangshanchang Fault, Dongtou Fault and Niulan River Fault, which are basically in accord with the geological structure framework of the mine area. Keywords: remote sensing; lineament; edge detection; wavelet transformation; multi-scale; image segmentation; hillshade

1 Introduction Lineaments refer to the linear or curvilinear textures on remote sensing image, which are shown as tonal mutation zone or boundary of different geological bodies(Solomon and Ghebreab,2006; De Oliveira Andrades Filho and De FáTima Rossetti,2012; Han et al.,2018). Their origin are varied, but most are related to structure factor. So most lineaments have advantage orientation that reflects the basic geological structure framework of a region and their general spatial distribution characteristics are often the response of deep crustal structures at the surface(Pour and Hashim,2014; Benaafi et al.,2017; Takorabt et al.,2018).Research on the geological lineaments can help to understand regional geological structure movement and evolution, and reveal the changing trend and distribution of various geological elements. Optical satellites images that represent reflectance and backscattering characteristics of the earth surface in response to electromagnetic waves at various wavelengths are generally used for lineaments extraction. The edges of remote sensing image, namely a region on which local gray value changing sharply, are important clue to the identify and extract of lineaments(Han et

First Author: Junlong Xu *Corresponding Author: Xingping Wen Haonan Zhang Jinbo Li Min Yu

E-mail: [email protected] E-mail: [email protected]

E-mail: [email protected] E-mail: [email protected] E-mail: [email protected]

Dayou Luo Lianglong Xu 1

E-mail: [email protected] E-mail: [email protected]

al.,2016; Romani et al.,2019). The study of geological structures by the means of remote sensing has been supported by the advances in computer hardware and spatial analysis technology(Pirasteh et al.,2013; Vasuki et al.,2014; Yeomans et al.,2019). Many edge detection algorithms have been used to make some achievements in automatic extraction of lineaments on remote sensing images(Hashim et al.,2013; Gazi et al.,2014; Sharifi et al.,2018; Saepuloh et al.,2018).However, there are rich ground objects, background information and complex texture on remote sensing image, then the shape of the geological bodies would be different at different observation scale. In addition, in the process of acquisition, reception and transmission of image data, noises would be added due to the influence of earth environment, satellite flight status, sensor accuracy and other factors(Wu et al.,2015).Under the circumstances, the conventional algorithms lack of automatic zoom function, so the noises and tiny textures that are irrelevant to geological structure would be wrongly detected as edges by the calculation at a single scale, and the amounts of edges extracted might more than the actual structures in the study area. Therefore, the idea of multi-scale edge detection is the key to solve the problem(Rosenfeld and Thurston,1971; Lopez-Molina et al.,2013; Liu et al.,2019).Wavelet has good time-frequency localization characteristics and multi-scale analysis performance, and the Mallet algorithm greatly promoted the application of wavelet theory(Morlet et al.,1982a, 1982b; Mallat,1989; Mallat and Hwang,1992; Mallat and Zhong,1992).Good results have been obtained in multi-scale edge detection on various images by wavelet algorithm(Tu and Karstoft,2015;Du et al.,2016; Azeroual and Afdel,2017; Boustani et al.,2019). So wavelet edge detection algorithm can be used to extract lineaments on remote sensing images. With the increasing of the scale, the structural pattern of the edges change, then the stable edges retained are more likely to be consistent with the actual geological structure at the same time as noise reduction. However, the extraction of lineaments are based on the variation characteristics of gray value on the image, if there are artificial buildings or luxuriant vegetation covering on the ground, it is difficult to extract the lineaments effectively. So the frequency, connectivity and authenticity of the lineaments is strongly affected by the source data and information of the earth surface. This may prevent plausible representation of tectonically significant lineaments relevant to long rock fractures and faults. Therefore, the result could be better by comprehensive using of multi-source data. Digital Elevation Model (DEM) is an advanced data for terrain analysis which provides a higher detailed terrain and structural information of the Earth surface by giving the definite elevation values in each pixel compared to optical satellite images such as Landsat. DEM are more practical because they are not affected by weather or other factors. Lineaments are often reflected as ridge and valley lines, so linear tectonic landform could be used to track fracture zones(Jordan et al.,2005; Soto-Pinto et al.,2013; Flores-Prieto et al.,2015). At present, most researchers use the rendering method of hillshade model alone based on DEM or combined with optical satellites images to extract lineaments(Bonetto et al.,2015; Radaideh et al.,2016; Masoud and Koike,2017; Das and Pardeshi,2018). In view of this, in this paper, we design and implement a new extraction method for lineaments based on optical satellites image and supplemented by DEM. Firstly, wavelet modulus maximum transformation was used to detect the multi-scale edges on Landsat 8 OLI image. Secondly, on the basis of quantitative analysis of the relationship and changing characteristics between the varied scale edge images, the 2D otsu algorithm is used to segment the optimal scale edge image with geological significance, then complete the preliminary extraction of lineaments. 2

Finally, the linear shadow map extracted from DEM and processed by mathematical morphology was superimposed on the preliminary lineaments map to generate a lineament map that is similar to the actual geological structure.

1 Study area and data 1.1 Study area The study area is Huize lead-zinc mine area with about 10km2 in Huize County, Qujing city, Yunnan Province, China, which is roughly falling in between 26°38 ' N to 26°40' N latitude, 103°43 'E to 103°45'E longitude. The main river within the study area is Niulan River(Fig.1).The Huize area is situated in the southwestern margin of Yangtze Block, the eastern part of the Kangdian Axis, which is in the southern part of Northeastern Yunnan Basin. It is located in a triangular area which is surrounded by the NS-direction crustal-scale Xiaojiang Fault at the Western region, the NE-direction crustal-scale Mile-Shizong Fault at the Southeastern region and the NW-direction Ziyun–Yadu Fault at the Northeastern region(Han et al.,2015). The study area which has undergone a long geological structure evolution presents complex topography with mountains piling up and ravines criss-crossing.

Fig.1.

Study Area

Because of large scale eruption of Emeishan basalt in Huize mine area, the lithology is mainly massive basalts and amygdaloidal (stomatal) basalts, which are mainly distributed in the northern part of Kuangshanchang Fault, the southwestern of the mine area and the peripheral areas. The geological structure of Huize area is characterized by developing large thrust nappe structure composed of NE-direction fold and faults. And faults can be divided into NW, EW, NE and NS-direction according to the sequence of formation. Due to the development of faults and the eruption of basaltic magma, The geology structure deformed strongly and the integrity of fold was destroyed. The typical faults in the mine area are Kuangshanchang Fault, Qilinchang Fault and 3

Dongtou Fault, which are accompanied by NW-direction small faults(Fig.2). The main faults in the study area are characterized by multi-stage activity and have undergone complex mechanical property transformation. As the result, most faults extend from the surface to the deep with the rock masses relatively fragmented, and the structural lenticular bodies are often seen. Although the study area is densely vegetated, Kuangshanchang Fault and Dongtou Fault destroied the integrity and continuity of the rock-soil bodies, then resulted in relative displacement and differential weathering between different parts of the geological body, which formed deep gullies and crushed zones; Niulan River Fault also caused "S" type sharp turn in the local section of the Niulan River. It provided useful information for extracting the lineaments by remote sensing.

Fig.2.

Geological map of Huize Mine Area

1.2 Image data The OLI (Operational Land Imager) on Landsat-8 Satellite can be used to acquire images in the range of the visible, near-infrared and short-wave infrared spectrum with high signal-to-noise ratio. OLI consists of 9 short spectrum segments with an image width of 185km *185km, the spatial resolution of panchromatic band is 15m, while the others are 30m; the radiation resolution is 12 bits, which greatly increases the grayscale level of the image and highlight the details of the objects(Chu et al.,2013). In this study, the OLI image with path number 129 and row number 42 we used was imaged on October 13, 2013, on which solar altitude angle was 51.61325972 and solar azimuth angle was 148.68991606 (Fig.3(a)). The digital elevation model we use is ASTER GDEM(V1), which is produced based on the detailed observation results of NASA's Terra satellite. The spatial resolution is also 30m(Fig.3(b)). All data are downloaded from Geospatial Data Cloud (http://www.gscloud.cn/). The infrared band are often used for geological structure study because of the strong ability for detecting the earth. After converting the data type of OLI image matrix into double-precision 4

type, the standard deviations of B5, B6 and B7 are respectively 0.1193, 0.1144, and 0.1134. It indicates that the absolute dispersion of B5 is the largest, and linear textures are most prominent. Therefore, wavelet edge detection was performed only on the B5 image.

Fig.3. (a) B5 image of OLI; (b) ASTER GDEM（V1）

2 Methodology 2.1 Lineament extraction based on wavelet transformation The image could be expressed in multiple scales and decomposed into approximate components and high-frequency components according to spatial resolution decreasing step by step through wavelet transformation, consequently, singularities or edges of the image at the detail level can be continuously focused on at any scale. Due to the convenience of calculation, the wavelet modulus maximum algorithm is often used for multi-scale edge detection of image. 2.1.1 Edge detection based on wavelet modulus maximum Wavelet multi-scale edge detection uses a smoothing function to smooth the image at different scales, then find the mutation point of the image according to the first or second order derivative of the smoothed signal, that is, detects the modulus maximum regions of wavelet transformation coefficients corresponding to the edge of image(Guo et al.,2010). Assuming Two-dimensional smoothing function θ(x,y) satisfies the following two expressions: ∞

∞

∫ ―∞∫ ―∞θ(x,y)dx=1

(1)

lim θ(x,y)=0

(2)

x,y→∞

Define the derivative of the smoothing function in both the x and y dimension as the B-spline basic wavelet : ψ(x)=

∂θ(x,y) ∂x

(3)

ψ(y)=

∂θ(x,y) ∂y

(4)

And we define the scale transformation of above expressions : 1

xy

∂θa(x,y)

(x) ψ(x) a =a2ψ (a,a)=

(5)

∂x

5

1

∂θa(x,y)

xy

(y) ψ(y) a =a2ψ (a,a)=

(6)

∂y xy

Where θa(x,y)=θ(a,a), a is scale of the wavelet transformation and generally taken as 2j, that is, binary wavelet transformation. Wavelet transformation with scale a is made on the image f(x, y) in both x and y direction: ∂

WT(x)f(a,x,y)=f(x,y)*ψxa(x,y)=a∂x(f(x,y)*θa(x,y))

(7)

∂

WT(y)f(a,x,y)=f(x,y)*ψya(x,y)=a∂y(f(x,y)*θa(x,y))

(8)

Where "*" stands for convolution operation. Two-dimensional wavelet transformation of the image f(x, y) is shown as follow: 1

WTf(a,x,y)= f(x,y)*ψa(x,y) =a2 ∬f(u,v)ψ(

x―u y―v a , a )dudv

(9)

According to the properties of the convolution, the above formulation could be expressed as vector form:

[

[

]

WT(x)f(a,x,y) =a× WT(y)f(a,x,y)

∂ ∂x[f(x,y) ∗ θa(x,y)] ∂ ∂y[f(x,y) ∗ θa(x,y)]

]

=a×grad[f(x,y)*θa(x,y)]

(10)

Where f(x,y)*θa(x,y) denotes to smooth the image,WT(x)f(a,x,y) and WT(y)f(a,x,y) respectively represent the gradient of the smoothed image along the x and y directions at scale a, i.e, the gradient component of WTf(a,x,y) along the horizontal and vertical directions. When a=2j, the binary wavelet transformation vector of the image f(x, y) is WT [WT

WTf(2j,x,y)=

(x)

f(2𝑗,x,y) f(2𝑗,x,y)

(y)

]

(11)

Its module can be expressed as: 2 MT2jf(x,y)= |WT(x)f(2𝑗,x,y)| + |WT(y)f(2𝑗,x,y)|

(12)

The magnitude of MT2jf(x,y) reflects the intensity of the grayscale change of f(x,y)*θ2j(x,y) at (x,y), its maximum value corresponds to the edge points of the image f(x,y) and the adjacent edge points can constitute the edge contour. While, noises that are also as the high frequency component of image may produce isolated false edge points after wavelet transformation. The multi-resolution representation of the image is actually a filtering process. Although the image can be down sampled to adapt to the increasing of the transformation scale when performing multi-scale filtering by spline wavelet filter, it may remove the pixels belonging to the edges, then affects the result of edge detection. Therefore, the spline filter is interpolated to adapt to the increasing of scale, that is, a 0 is inserted between two elements of the filter sequence when scale increase. 2.1.2 Image segmentation of 2d otsu Scale characteristics of edges reflect different singularity of lineaments. With the increasing of scale, the amount of edges detected decrease. After analyzing of multi-scale characteristics, the optimal scale edge image could be determined. Thus pixel gray value of the edge and neighborhood or the background area and neighborhood are relatively close, while noises and neighborhood exist large difference.2D histogram can be established according to the distribution 6

of the center pixel value and the mean value of neighborhood, and 2D otsu can be used for image segmentation, which can filter out the interference information and extract the lineaments with geological significance. The threshold is a two-dimensional vector, and the optimal threshold is obtained when a two-dimensional measure criterion takes the maximum value(Sha et al.,2016). The formula is as follows: T=ωb[(μbf-μf)2+(μbg-μg)2]+ωo[(μof-μf)2+(μog-μg)2] (13) Where ωb is the ratio of background pixels, ωo is the ratio of edge pixels, (μf,μg) is the two-dimensional total mean, (μbf,μbg) is the two-dimensional background mean, (μof,μog)is the two-dimensional edge mean. 2.2 Lineaments aided tracking based on hillshade The tectonic activity is closely related to the natural landscape of the earth's surface such as topography and landform, while the DEM data can reflect the geomorphological features, and the fracture zones can be found according to the abnormal landscape of the tectonic landform. Faults developed strongly in Huize mine area with obvious linear landform. The hillshade can be extracted by simulating the sun light on DEM, which increase the three-dimensional sense of the mountain to highlight the lineaments information.The negative landform are most prominent in the shadow map, and lineaments which are perpendicular to the direction of illumination are enhanced most obviously. Therefore, linear hillshade extracted from DEM could not only aid to track the geologic structure, but also detect hidden large fracture zones.

3 Simulation experiment and analysis 3.1 Multi-scale edge and characteristics analysis The result of wavelet edge detection are shown in Fig.4. The wavelet transformation from Level 1 to Level 4 can clearly highlight the edges of the image. The width, length and distribution of the edges have changed significantly, and it reflect different structural characteristics of remote sensing image at different scales and achieved the purpose of adaptive multi-scale edge detection. The edge image with small scale has a narrow pixels that position accurately, but it is weak in suppressing image tiny textures and noises. The distribution of edge points are isolated, discontinuous, and fragmented, as shown in Fig.4(a)and Fig.4(b), the weak and small edge pixels are almost all over the image, which are distributed randomly and lack of useful information. The amount of tiny edges gradually decrease with the scale increasing, at the same time, the remaining edges gradually become thicker. Visual sense presented in Fig.4(d) is fuzzy and the positioning accuracy of the edges reduce, but the outline that consist of the edge pixels is roughly corresponding to the geological structure of mine area. It indicates that the edge image of wavelet transformation at Level 4 remains significant high-frequency components while suppressing noises, which contain less impurities in edge information.

7

Fig.4. wavelet transformation edge of Level 1(a), Level 2(b), Level 3(c) and Level 4(d) In order to objectively evaluate the results of wavelet edge detection, mathematical methods are needed to quantitatively analyze the edge features at each scale. The key of multi-scale feature analysis is to find out the changes in geological bodies composed of edge pixels at different scales. Thereby Hu Invariant Moment with translation invariance, scale invariance and rotation invariance could be used to describe and analyze the shape and structure of wavelet edge images of 4 scales.Hu Invariant Moment consist of φ1~φ7, the low-order moment mainly describe the general characteristics of the image such as area, principal axis, direction Angle, etc., which are less affected by noises, while, high-order moment mainly describe the details of the image and are more susceptible to noises(Hu,1962).

8

Fig.5. Hu Invariant Moment Fig.5 shows that: (1) φ1 and φ2 of the edge image of 4 scales change little, and φ3 change significantly with the scale increasing. The differences of φ4, φ5, φ6 and φ7 at different scales are more obvious. It indicates that the general feature of the geological bodies that consist of the edges at different scales in the same image are consistent, but the local detail features change significantly; (2) Observing the 7 curves, with the scale increasing, the values of φ1～φ7 (except φ3) generally show a monotonous increasing trend, and the range of the invariant moments are generated between the edge of Level 1 and Level 4 (except φ3), which indicates that the larger the difference of scale, the larger the difference in edge information. As the result, the differences between the edge images of Level 1 and Level 4 is the largest.

Fig.6.

difference of Invariant Moment on adjacent scale edge

Fig.6 shows: (1)The curves almost coincide with each other at the φ1 and φ2, because the general shape of the geological bodies on edge images of the adjacent scale are very close; (2) With the scale of wavelet transformation increasing, the difference between the values of high order moment at the adjacent scale edge images becomes smaller, which indicates that the changing trend of shape details of geological bodies on edge images tends to be gentle. Table 1 Basic Statistical mean

range

std

cv

entropy

edge of Level 1

0.0085

0.0707

0.0065

0.7568

2.5113

edge of Level 2

0.0080

0.0502

0.0058

0.7176

2.4033

edge of Level 3

0.0076

0.0485

0.0054

0.7022

2.3235

edge of Level 4

0.0073

0.0354

0.0051

0.6985

2.2594

Table 1 shows: (1)mean of the wavelet edge coefficient decrease gradually, which indicates that the center position of gray data presents a downward trend; (2)range decrease gradually, which indicates that the interval of gray data becomes narrower; (3)standard deviation(std) 9

decrease gradually, which indicates that the absolute dispersion degree of gray data decrease; (4)coefficient of variation(cv) decrease gradually, which indicates that the relative dispersion degree of gray data decrease;(5) The entropy decrease gradually, which indicates that the ordering degree of the system is getting higher and higher, the closer the gray level distribution of the image is, the smaller the uncertainty is. Therefore, it can be reasonably speculated that with the scale increasing, the type of residual edges tend to be consistent. Table 2 Correlation Coefficient edge of Level 1 edge of Level 2

edge of Level 1

edge of Level 2

edge of Level 3

edge of Level 4

1

0.602

0.286

0.111

1

0.661

0.281

1

0.636

edge of Level 3 edge of Level 4

1

It can be seen from Table 2: (1)all the correlation coefficients of the wavelet transformation edges at adjacent scales reach 0.6 or more, and there is a moderate positive correlation between them; (2) but regard the edge of Level 1 as the standard, with the scale increasing, the correlation gradually decreases from 0.602 to 0.111.There is basically no correlation between the edges of Level 1 and Level 4,which indicates that the essential attributes of edges has qualitative change when transforming to Level 4. The results of multi-scale characterics analysis show: with the scale increasing, the high-frequency component in the original image reduce rapidly, while, the trend of decline is slower for large edges. So the signal-to-noise ratio increases. When wavelet transformation reaches to Level 4, the contour of edge images remains basically consistent, the changing trend of image details has been weak. So the large edges are well preserved while removing noises and tiny edges. The optimal scale of edge detection based on wavelet modulus maximum is 4. Thus the type of edges on the scale tend to be uniform and contain useful geological information. 3.2 Segmentation of edge image The 2D otsu algorithm may ignore the small targets on the image. The amount of non-large edge pixels on the edge image of Level 4 is less and the connectivity is poor, which will not play a big role in the threshold iterative calculation process. Therefore, image segmentation would filter out the noises and tiny edges again.

10

Fig.7.

(a)image segmentation result; (b) filtering out discrete pixels after segmentation

Fig.7(a) roughly reflects the general tectonic pattern with spatial distribution of NE direction in Huize mine area, which denotes that the 2D otsu algorithm make a good effect to segment the edge image of Level 4. And the remaining short extremum chain can be removed by mathematical morphology filtering, the rest are large lineaments. Although discrete pixels are filtered out, the edges with NE direction in Fig.7(b) corresponding to the Kuangshanchang Fault in Fig.2 is disconnected because of artificial buildings covering onto surface. So discontinuous lineaments can be connected through hillshade extracted from DEM. 3.3 Extraction of linear shadow The general tectonic direction of Huize mine area is NE, and there are also faults of SN and NW directions. So we set solar altitude 45°, and the solar azimuth 292.5°, then obtain hillshade map from DEM.

Fig.8. linear shadow (a)density segmentation; (b)threshold segmentation; (c)filling ; (d)dilating In Fig.8(a), the highlighted orange linear shadow can be seen. After segmenting the image and filtering out the discrete pixels with small areas, we can see the binarization linear shadow in Fig.8(b), which shows that shadow of NE and near NS direction present a potential intersecting trend. So use mathematical morphology algorithm such as ‘filling’ and ‘dilating’ to make them 11

intersect(Fig.8(c), (d)). Finally, the optimized lineaments were obtained by ‘slimming’ after overlaying Fig.8(d) onto Fig.7(b).

4 Result The result of automatic extraction of lineaments are shown in Fig.9, and the broken sections of the NE lineament are connected through aided tracking by hillshade. The tectonic traces which show the form with northward convergence and southward spreading are clear, which corresponds to the geological structure framework in Huize mine area(Fig.2). Especially the triangular region enclosed by the edge pixels in the central area of Fig.9 reflects the intersecting relationship between Kuangshanchang Fault with NE direction, Dongtou Fault with near NS direction and the NW direction fault. By observing Fig.2 and Fig.3(a), we know the existence of the Niulan River Fault with near NS direction makes the erosion ability of the local part of the river stronger and produces ‘S’ sharp turn. Thereby, to some extent, the evolution of the Niulan River is controlled by geological structure. It failed to extract Niulan River Fault by wavelet transformation, but effectively extracted its associated valley landform. The near NS direction lineament extracted on the right side of Fig.9 is the deep valley, and the rest are the gully line of concave bank and the ridge line of convex bank at the maximum point of ‘S’ sharp turn. This part of lineaments indirectly verify the existence of Niulan River Fault. Due to the dense vegetation covering on the region where Qilinchang Fault locates, there were no obvious tonal textures, structural textures and linear gullies, which lead to Qilinchang Fault could not be extracted.

Fig.9.

lineaments

5 Discussion The scale of remote sensing mainly involves the spatial resolution of images and the size of the geological bodies themselves. The edge types of remote sensing images would show obvious differences at different scale, that is, at one scale, it is the structural component, while at another scale, it may become the noise component. In addition, remote sensing images are a kind of 12

two-dimensional non-stationary signal, although high frequency components contribute significantly to image detail, they are mixed with noises. Predecessors often used edge detection method such as gradient operator, segment tracing algorithm and so on to extract lineaments, which are sensitive to noises, and the single-scale calculation process cannot automatically filter edge information. Lineaments extracted are not only redundant, but also has no obvious correspondence with actual geological structures. Therefore, noise elimination and scale transformation are the main problems for lineaments extraction, the key is only to detect those edges that we are interested in and filter out meaningless pseudo-edges. Wavelet edge detection algorithm can be used to obtain multi-scale expression of the edge information on the original single spatial resolution image, which would reflect the multi-scale structure characteristics of the image. With the increasing of transformation scale, noise points and tiny textures are effectively suppressed, and the signal-to-noise ratio increases. The edges of Level 4 are thick and stable and the type of edge tend to be uniform (Fig.4, Fig.5, Fig. 6, table 1, table 2). The similarity between the contour outlined by the edge pixels and geological structure in Huize mine area increase, which indicates that the first part study was successful. However, the lineaments obtained by the 2d otsu algorithm on the edge image of Level 4 is incomplete. And the study area is located in a mountainous area, where there are deep gullies and other linear negative landform. Therefore, hillshade are obtained on DEM by setting solar altitude angle 45°and solar azimuth angle 292.5°(almost perpendicular to the direction of main geological structural in study area). The difference of shadow brightness sharply highlights the linear tectonic landform with NE direction related to the Kuangshanchang Fault and linear tectonic landform with near NS direction related to the Dongtou Fault, which indicates that the second part study was also successful. It should be pointed out :(1) When wavelet transformation reaches Level 4, The image loses a lot of details accompanied by the edges misaligning. (2)Tectonic landform are the traces on the surface derived from deep structure, to some degree, between which there must be differences in spatial location and pattern.(3)The spatial form of shade will change with the changing of illumination condition we set.(4)Mathematical morphological filtering would make objects information deform and distort. Therefore, there will be some deformation and displacement between the lineaments extracted and the actual geological structure.

6 Conclusion Based on the scale effect and noises problems that exist in remote sensing, this study used landsat-8 OLI and DEM to extract the lineaments in Huize mine area where geological structure and linear tectonic landform extremely developed. Edge of 4 scale which reflect different structural characteristics of remote sensing images are detected by wavelet modulus maximum algorithm. The change of scale directly affects the detail effect of detection. At small scale, fine edges accompanied by lots of noises can be seen, while, thick edges that are detected at a large scale are stable. Multi-scale characteristics analysis of edge images is carried out by Hu Invariant Moment, mean, range, standard deviation, coefficient of variation, entropy and correlation coefficient. The analysis result show that, with the scale increasing, the signal-to-noise ratio increases. When wavelet transformation reaches to Level 4, the contour of the edge image remains basically consistent, and the changing trend of image details has been weak. Thus the type of edges at the scale tend to be uniform and contain useful geological information. The outline surrounded by the edge pixels roughly coincides with the 13

geological structure framework in mine area. Because of artificial buildings covering on the surface, it is not complete to extract the lineament on edge image of Level 4 by using 2D otsu. Then DEM was used to extract hillshade map, and the difference of shadow brightness sharply highlighted the linear negative landform with NE direction and near NS direction. The linear shadows are generated by threshold segmentation, filling , dilating and filtering of discrete pixels. The optimized lineaments map is obtained by superimposing 2 types of lineaments information.Lineaments are in good correspondence with Kuangshanchang Fault, Dongtou Fault and Niulan River Fault, which shows that it is feasible to extract the lineaments with practical geological significance through wavelet edge detection and aided tracking by hillshade.

Acknowledgement This paper is jointly funded by provincial innovation team of the geological process and mineral resources in Kunming University of Science and Technology and the remote sensing geochemistry team in Kunming University of Science and Technology, which I here with best thanks.

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