pal_et_al[1] sar fft lineament ijrs

8/2/2019 Pal_et_al[1] SAR FFT Lineament IJRS

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Extraction of linear and anomalous features using ERS SAR data over

Singhbhum Shear Zone, Jharkhand using fast Fourier transform

S. K. PAL{, T. J. MAJUMDAR*{ and A. K. BHATTACHARYA{

{Department of Geology and Geophysics, Indian Institute of Technology,

Kharagpur721 302, India

{Earth Sciences and Hydrology Division, Marine and Water Resources Group, Remote

Sensing Applications and Image Processing Area, Space Applications Centre (ISRO),

Ahmedabad380 015, India

(Received 14 July 2005; in final form 24 February 2006)

Digital filtering of ERS-2 SAR data using the fast Fourier transform (FFT) has

been attempted over Singhbhum shear zone (SSZ) and its surroundings for

extraction of linear and anomalous patterns. The results show that numerous

lineaments as well as drainage patterns could be identified and demarcated by

FFT digital filtering method. Major as well as several minor drainage patterns

are easily detectable from the filtered image, which are structurally controlled

and not observed in the original map. Comparison of the present interpretation

of the study area to existing geological map/earlier interpretation has been done

effectively. This technique was found to be more effective in identifying the

lineaments using ERS SAR data compared with using Landsat imagery over

the study area. The present study reveals that maximum lineaments occurring in

the north of SSZ are NNE, NNW and NW trending, while maximum lineaments

occurring in the south of SSZ are NE, ENE, WNW, and NW trending. The

demarcated geological structures may have a great significance to locate the

hidden ore/mineral occurrences. The existences of various mines, such as

Baharagora, Mosaboni, Surda, Narwa, Bhatin, Jadugoda, Rakha, and

Tatanagar along the shear zone, correlate well with the interpreted results.

1. Introduction

Linears are naturally/culturally occurring features observed in remote sensing

imagery. They are seen in remotely sensed images as a simple or composite linearfeature on the surface. Their parts align in straight or slightly curving relationships

that differ distinctly from the patterns of adjacent features in various combinations

of stream patterns, tonal changes or tonal vegetation and topographic alignments.

Presumably, a lineament expresses a subsurface phenomenon (Sabins 1997).

Lineaments, which may be continuous or discontinuous, under certain circum-

stances, may be regarded as the surface manifestation of fault and fracture zones.

These have been linked with local or regional tectonics and used as potential zones

for oil, gas and mineral exploration (Rakshit and Swaminathan 1985, Mah et al.

1995, Majumdar 1995, Sabins 1999, Briere and Scanlan 2000, Chernicoff et al.

2002).

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 27, No. 20, 20 October 2006, 45134528

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2006 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160600658172


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Radar is an active system which illuminates the surface with a beam of microwave

radiation. Radar is most sensitive to surface roughness and soil moisture differences

(variation in the complex dielectric constant which is a measure of the electrical

properties of surface materials). Radar can penetrate the surface micro-layer in the

soil covered areas (Prost 2001). Lineaments are extremely well manifested on SAR

images, and on several occasions structural features; for example, fractures, folds,faults etc. have been detected, as well as extended in SAR imageries. Also, look

angle and direction have a major impact on the response and manifestation of

surficial features in SAR imageries. Earlier results have shown that RADARSAT-1

C-band horizontally polarized images have been very useful for geomorphology,

geological structures and rock units mapping (Singhroy and Molch 2004, Harris

1984, Lowman et al. 1987, Masuoka et al. 1988). The SAR image is more effective

than optical imagery for studying features such as surface roughness and

topography. This is due to variation in radar backscatter as a function of

wavelength (C-band , 5.6 cm), incident angle and polarization. Useful information

on terrain morphology and surface relief (related to geological structure) is providedby SAR imagery, due to effect of radar backscatter sensitivity to slope angle and to

shadow effects caused by topographic relief (ERS-2 SAR website). An image

transform (viz. FFT, Hadamard, Haar) is a 2D spectrum derived from the

decomposition of the image data which can be utilized to extract features from

images (Pratt 1978, Majumdar 1995, Majumdar and Mohanty 1999).

2. Geological setup of the area

The regional geotectonic/geological map (Saha 1994) of the study area derived from

Landsat imagery and ground data has been presented in figure 1. The area has been

extensively surveyed using ground-based geological (Dunn 1929, Sarkar 1963, Naha

1965, Saha 1994) techniques. It has a major tectonic element (Singhbhum Shear

Zone) that separates the cratonic block (Singhbhum-Orissa Iron Ore Craton) in the

south from the Proterozoic mobile belt (Singhbhum Mobile Belt) in the north. It

runs in a northward dipping direction along a northwardly convex arcuate belt for a

length of more than 160 km from Bharagora in the east to Chakradharpur in the

west. The Singhbhum Shear Zone occurs as a curvilinear belt with an EW trend.

Singhbhum rocks, like those of other Precambrian terrains, have undergone many

phases of deformation and metamorphism. Rocks to the south of the Singhbhum

Shear Zone are relatively less metamorphosed compared with those to the north.

Rocks of Dhanjori Group are exposed in the southern part of Singhbhum Shear Zone.

This group consists of conglomerate, arkose, quartzite and lava flows. The equivalent

of the bottom part of this succession is identified as the Singhbhum Group to the

north of Singhbhum Shear Zone. Similarly, the equivalent of lava flows in the north is

called Dalma Lava. Dolerite dikes have intruded in the Singhbhum Granite and occur

mostly in the southern part of Singhbhum Shear Zone.

3. Data sources and area of interest

ERS-2 SAR Path Radiance Image (PRI)/Precision Image (Path: 0842; Row: 0198)

of 30 September 2002 over the Singhbhum Shear Zone and its surroundings,covering an area between latitudes 22u159N t o 2 3uN, and longitudes 86uE to

86u459E, has been used in this study (shown in the box in figure 1). The Precision

Image is a path oriented and system corrected product, being the basic product used

4514 S. K. Pal et al.


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for a variety of remote sensing applications. Scene size is 100 km in range direction

and at least 102.5 km in azimuth direction. Spatial resolution is 25 m in range

direction and 15 m in azimuth direction. The registration of SAR imagery with the

rectified IRS (Indian Remote Sensing Satellite)-1C imagery has been accomplished

through image-to-image co-registration using nearest neighbourhood re-sampling

technique. The rectified precision image has been transformed from spatial domain

to frequency domain, i.e. Fourier transformed image using the fast Fourier

transform (FFT) method. Thereafter, power spectrum has been generated from this

Fourier transformed image. This power spectrum has been edited to enhance the

linear and anomalous patterns, such as, structural and tectonic configuration of thearea.

Singhbhum, particularly the western part, is full of hills alternating with valleys,

steep mountains, and deep forests on the mountain slopes. Singhbhum contains best

Sal forests and the Saranda (seven hundred hills) forest area is well known world

over. Climatologically, the study area may be divided into three seasons: Winter

from November to February, summer from March to May, and the rainy season

from June to October. The cold season is delightful while it is unpleasantly hot in the

summer season with hot westerly winds prevailing. On account of the barrier of hills

in the southeast, the atmosphere is generally dry. The rainfall is the highest in July

and August. Monsoon generally breaks in the second week of June. December andJanuary are the coldest months while April and May are the hottest.

The soil in Singhbhum has been classified mainly into three groups: rocky, red

and black soils. Rocky soil remains practically uncultivated. Red soil is spread

Figure 1. Geological map over the study area (after Saha 1994).

Extraction of linear and anomalous features using ERS SAR data 4515


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throughout the area: it is sandy and loamy and has poor fertility. Black soil is very

fertile. Rice is the main crop.

Howrah-Nagpur and Rajkharsawan-Chaibasa-Gua railway lines are mainly used

for mineral transportations. Apart from them, there are a number of forest roads.

Singhbhum is rich in natural resources, both for minerals and forest produce.

4. Methodology

The steps involved in enhancing a digital image, f(x, y), using frequency domain

technique are: (i) to compute Fourier transform F(u, v) of f(x, y) by FFT method, (ii)

to multiply the obtained F(u, v) with a filter function H(u, v), and finally (iii) to take

the inverse Fourier transform of G(u, v), i.e., product of F(u, v) and H(u, v). The

flowchart for digital image enhancement using FFT method has been presented in

figure 2. Interpretation of the FFT filtered imagery by visual pattern recognition of

surface features resulting from variations in radar backscatter in the source image as

well as from differences in surface roughness and topography, is carried out bystudying tone, and textural variations (Masuoka et al. 1988, Paganelli et al. 2003,

Singhroy and Molch 2004).

Figure 2. Schematic diagram for FFT filtering technique.



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4.1 Fast Fourier transform

Fast Fourier transform (FFT), a classical image filtering technique, is used to

convert a raster image from the spatial domain into a frequency domain image. The

FFT calculation converts the image into a series of two-dimensional sine waves of

various frequencies. The Fourier image can be edited accordingly for imageenhancement such as sharpening, contrast manipulation and smoothing. Sharpening

is achieved by using a high-pass filter whose function is to attenuate low frequencies,

whereas image smoothing is done by low-pass filter. Sometimes combination of both

low-pass as well as high-pass filters, known as band pass filter, is used. In the

frequency domain, the high-pass filter is implemented by attenuating the pixel

frequencies with the help of different window functions, viz. Ideal, Bartlett

(Triangular), Butterworth, Gaussian, Hanning and Hamming etc. (ERDAS 2001).

Let us consider a function f(x, y) of two variables x and y, where x50, 1, 2, , N

2 1, and y50, 1, 2, , M2 1. The function f(x, y) represents digital value of an

image in the xth row, yth column; Mand Nare the maximum numbers of rows and

columns in the image which are multiple of two. Then the forward Fourier

transform of f(x, y) is defined as (Gonzalez and Woods 1992, Jahne 1993)

F u, v ~ 1MN

XM{1

x~0

XN{1

y~0

f x, y exp{j2p ux=Mzvy=N 1

for u50, 1, 2, , N21, v50, 1, 2, , M 2 1 and j~ffiffiffiffiffiffiffiffi{1

p; u and v are the

frequency variables.

The inverse Fourier transform of F(u, v) returns to f(x, y) which is defined as

f x,y ~XM{1

u~0

XN{1

v~0

F(u, v)expj2p(ux=Mzvy=N) 2

for x50, 1, 2, , N2 1, and y50, 1, 2, , M2 1. Equations (1) and (2) are known

as frequency transform pair.

4.2 Interpretation of ERS SAR FFT filtered imagery

The dielectric constant of a rock at radar wavelength is specially influenced by the

water content of the rock. Dry rock has a dielectric constant of the order of 38,

whereas that of water is 80. With increasing moisture content in the rock the

dielectric constant will increase almost linearly. De Loor (1982) has given a generalreview of dielectric properties of wet materials. The depth penetration in soil and

rock material is inversely related to the dielectric constant, but directly to the radar

wavelength used. In moist rocks and soils the depth penetration will be only skin-

deep. In dry sand area a reasonable penetration can be obtained with the use of

radar of longer wavelength (Koopmans 1983).

The most important radar parameters for lineament mapping are:

(i) look direction, which determines the preferential enhancement of the terrain;

(ii) incidence angle, which affects the topographic enhancement; and

(iii) spatial resolution, which affects the amount of fine structural detail to be

seen (Harris 1984).

Rock type (lithology) has no obvious effect on radar return in the area with forest

cover as is the case in Singhbhum. However, structure and major lithologic units can



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be delineated through their topographic expression (Lowman et al. 1987, Sabins

1999).

The filtered images have been interpreted for identification of structural features,

lineaments and fracture/fault planes. The lineaments are readily identifiable surface

features due to tone, contrast and textural variations associated with topographic

variation, lithological transition, and drainage patterns, whereas fracture planes and

(or) fault planes are linear features along the offset between sets of lineaments. The

resulting lineaments have been analysed in regional geological context, compiled,

and plotted on Rose diagrams to outline the variability in strike directions. The

identified lineaments have been compared with the known regional structural

trends.

Figure 3. Filtered enhanced image of the study area after IFT of filtered power spectrumusing (i) low-pass filter with Butterworth window (D053500, LFG51, HFG50) and (ii) high-pass filter with Ideal window (D0550, LFG50, HFG51).



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5. Results and discussion

ERS-2 SAR path radiance data of the study area has been transformed into Fourier

image using FFT. Fourier image has been edited in frequency domain and then

transformed back to spatial domain to get the enhanced linear and anomalous

patterns. In order to band pass the particular frequency range through this Fouriermagnitude image (power spectrum), lower frequency components of radius 3500

(D025u2 + v2) has been suppressed using a low pass filter with the help of

Butterworth window function (low frequency gain, LFG51.5, high frequency gain,

HFG50) and high frequency components of radius 50 has been suppressed with the

help of Ideal window function (LFG50, HFG51.5). Finally the filtered spectrum is

transformed back to spatial domain to obtain the Fourier filtered image (figure 3).

Similarly, some other band pass filters have also been tested with different radii, viz.,

using various window functions (figures 4 and 5) for better topographic as well as

Figure 4. Filtered enhanced image of the study area after IFT of filtered power spectrumusing (i) low-pass filter with Butterworth window (D053500, LFG51.5, HFG50.3) and(ii) high-pass filter with Ideal window (D05100, LFG50, HFG51.5).



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surface features enhancement, which aids in interpretation of lineaments expressedby physiographic features, surface geology transitions and/or associated vegetation

coverage variations. The filtered images exhibit numerous linear and anomalous

patterns over the study area (figures 35). The interpreted linear and anomalous

features have been overlapped on the enhanced SAR image (figure 6). Finally, the

interpreted map of the lineament and drainage patterns in the study area has been

prepared and presented in figure7. However, since the look-direction is towards west in

ERS SAR image with descending (NS) passes, main features including the Singhbhum

Shear Zone, Dalma Thrust Belt, Rakha mines area (Dhanjori formation), and the

Subarnarekha river which are almost perpendicular to eastwest direction have been

enhanced (figures 1, 6 and 7). Also, rock type (lithology) has no obvious effect on radarreturn in the area with forest cover as is the case in Singhbhum (Lowman et al. 1987,

Sabins 1999). But orientation of radar look direction to the topographic and tectonic

grain of the terrain is useful for studies in structural geology (Harris 1984).

Figure 5. Filtered enhanced image of the study area after IFT of filtered power spectrumusing (i) low-pass filter with Gaussian window (D054000, LFG52, HFG50.1) and (ii) high-pass filter with Ideal window (D05300, LFG50, HFG52).



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The topographic enhancement reflected by brightness contrast, texture and tonal

variation between Singhbhum-Orissa Iron Ore Craton in the south and SinghbhumMobile Belt in the north, interpreted as Singhbhum Shear Zone (SSZ). The

structural mapping of the study area has been divided into two major parts, north of

the Singhbhum Shear Zone and south of the Singhbhum Shear Zone. In the

southern part of SSZ, WNW, NW, and NNW trending lineaments and NE trending

fractures/faults are delineated by brightness contrast and texture variation over the

Dhanjori group of meta-volcanics and meta-sediments. The NE, NNE, ENE, and

NW trending lineaments are observed over Singhbhum granite and Gurumahisani

Group (figures 6 and 7). Besides, a few folds having closures towards the east with

axial plane traces running EW have been identified in the southern part of SSZ. In

the northern part of SSZ, lineaments over the Dalma Volcanic have been tracedalong the major Dalma fold. These lineaments have NW, NNW, NE, and NNE

trends. Some NE and NW trending fractures/faults have been delineated across

these lineaments. Another prominent shear zone has been identified due to high

Figure 6. Overlap of structural features on the FFT enhanced ERS-2 SAR imagery.



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tonal and textural variations between Dalma Volcanic and Singhbhum Group.

Kuilpal granite in the centre of Singhbhum group occurring north of DalmaVolcanics, has been demarcated by the distinct lineaments. Some folds having

closures towards south with axial plane traces trending NS, are observed over the

Dalma volcanic range, whereas some other folds, having closures towards east, with

Figure 7. Structural map of the study area as interpreted from the FFT filtered ERS-2 SARimage.



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axial plane traces running EW, are identified over the Singhbhum group in the

north-west part of the study area. High contrast and darker tone emphasize the

main drainage pattern of the study area. Summary of lineament orientations is

presented in table 1. The synoptic Rose diagrams of lineaments and fractures/faults

orientation have been presented in figure 8. A total of 805 structural features have

been identified, out of which 695 are linear features (lineament/fault/fracture) and110 are folds/curvilinear features. The identified linear features have vector mean of

value 88 with circular variance 0.59 and circular standard deviation 78. The number

of linear features identified in the north of SSZ is 382, which have a vector mean of

value 156 with circular variance 0.32 and circular standard deviation 49, whereas the

number of linear features identified in the south of SSZ is 313, which have a vector

mean of value 81 with circular variance 0.27 and circular standard deviation 45. The

identified linear features in the north of SSZ have greater circular variance as well as

greater standard deviation than that of linear features in the south of SSZ which

clearly indicate that the rocks in the area north of SSZ have undergone several

phases of deformation compared to the rocks in the area south of SSZ (Sarkar andChakraborty 1982).

From the overall study, it is clear that maximum lineaments occurring in the

north of SSZ are NNE, NNW and NW trending, while maximum lineaments

occurring in the south of SSZ are NE, ENE, WNW, and NW trending. The major

river in the study area, Subarnarekha, as delineated from the filtered SAR imagery is

observed to be running almost parallel along the northern boundary of SSZ for

some distance, and then following intermittently in between the SSZ and Dalma

volcanic (figure 6). High contrast and darker tone emphasize the main drainage

pattern of the study area. It can be concluded that the major river, Subarnarekha

and its tributaries, are structurally controlled. Some short length seasonal streams

Table 1. Summary of lineaments orientation as identified from FFT filtered ERS-2 SARimagery.

Lineament trendTotal number of

lineamentVectormean

Circularvariance

Circular standarddeviation (u)

South of SSZNorth-northeast 26 18 0.01 9North-east 81 46 0.01 9

East-northeast 70 77 0.01 9West-northwest 65 105 0.01 9North-west 57 134 0.01 8North-northwest 14 163 0.01 7Total lineament to the southof SSZ

313 81 0.27 45

North of SSZNorth-northeast 92 15 0.01 9North-east 51 42 0.01 8East-northeast 55 72 0.01 9West-northwest 50 106 0.01 8North-west 50 135 0.01 9

North-northwest 84 163 0.01 7Total lineament to the northof SSZ

382 156 0.32 49

Total lineament 725 92 0.62 82



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are also found to be structurally controlled in the south-western part of the studyarea as observed from the digitally enhanced image. Subarnarekha river crosses

Dalma volcanic ridge near Burudhi at an obtuse angle, while one tributary of the

Subarnarekha river, Dudh Nadi crosses the Dhanjori volcanic ridge almost

orthogonally, which indicates the existence of a prominent fractures/faults in this

region (Geological Survey of India 1998). In addition, numerous fractures/cross-

fractures/faults have also been mapped over the study area from the filtered SAR

imagery, as can be seen from figures 6 and 7. These demarcated structures have great

significance from the economic point of view since they can be host to various

mineralized bodies along the weak zones. The various mines, such as, Baharagora,

Mosaboni, Surda, Narwa, Bhatin, Jadugoda, Rakha, and Tatanagar are along theshear zone, which are also identifiable from the processed SAR image.

The structural interpretation map of a part of the present study area, as generated

by Majumdar (1995) using FFT techniques on Landsat imagery and the

Figure 8. Synoptic Rose diagram representing different lineament trends interpreted onFFT filtered ERS-2 SAR imagery.



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corresponding Rose diagram have been presented in figures 9 (a) and (b), whereas

the structural map of the same area interpreted from the present study and the

corresponding Rose diagram are shown in figures 10 (a) and (b). The comparison of

figures 9 and 10 reveals that the present study is more effective for delineation ofstructural features.

6. Conclusions

The present study shows that digital filtering technique using fast Fourier transform

on ERS SAR imagery is an effective tool for extraction of linear and anomalous

Figure 9. (a) Structural interpretation map of a part of the present study area carried out byMajumdar (1995) on Landsat imagery using FFT techniques. (b) Synoptic Rose diagram.

Figure 8. (Continued.)



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patterns in a tectonically disturbed area. The present study reveals that maximum

lineaments occurring in the north of SSZ are NNE, NNW and NW trending, while

maximum lineaments occurring in the south of SSZ are NE, ENE, WNW, and NW

trending. Also, numerous linear features, faults/fractures and folds could be

identified and demarcated using this technique on SAR imagery which may have a

great significance to locate the hidden ore/mineral occurrences. Since a number of

transformations have occurred during last 1800 Ma in SSZ because of collisions of

two plates, identification of these linears and comparison with earlier results will be

helpful for tectonic studies in this region. Corresponding Rose diagrams are very

helpful for quantification of lineament occurrences. Major, as well as several minor,

drainage patterns which are structurally controlled, are easily detectable in thefiltered image. It is found to be more suitable and effective in delineating lineaments,

as well as drainage patterns, in the study area using ERS SAR data than that

obtained earlier from Landsat imagery using the same FFT technique. However,

cultural linears/systematic noise patterns will also be extracted which need to be

discarded during final interpretation.

Acknowledgements

The authors wish to thank the anonymous referees for their critical comments and

suggestions for the improvement of the manuscript. They are also thankful to Dr R.

R. Navalgund, Director, SAC, Dr K. L. Majumder, Deputy Director, RESIPA/SAC and Dr S. R. Nayak, Group Director, MWRG/RESIPA for their keen interest

in this study. Thanks are due to Shri R. Bhattacharyya and Shri S. Chatterjee,

ESHD for their help at various stages of this activity.

Figure 10. (a) Structural map of the same area as interpreted in the present study. (b)Synoptic Rose diagram.



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4528 Extraction of linear and anomalous features using ERS SAR data

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