Merging seasat and SPOT imagery for the study of geological structures in a temperate agricultural region

REMOTE SENS. ENVIRON. 43:265-279 (1993)

Merging Seasat and SPOT Imagery for the Study of Geological Structures in a Temperate Agricultural Region H. Y6sou,* Y. Besnus,* J. Rolet, t J. C. Pion, * and A. Aing ~ • Groupement Scientifique de T6ldddtection Spatiale and Laboratoire de Tdldddtection et de G~ologie StrtuTturale, Institut de G~ologie, Strasbourg, France t D ~ p a r t ~ t des Sciences de la Terre and Groupement "Ouest-Image," Brest, France •LIA, Unitd de T616d6tection, ORSTOM, Bondy, France

T h i s study highlights advantages of using radar data combined with multispectral data to improve the interpretation of the geology over temperate agricultural regions. After speckle reduction and geometric correction, a Seasat image was combined with a multispectral SPOT image in order to enhance geological features. Two different merging procedures have been examined: photographic and numerical procedures. The photographic techniques consist in assigning the cyan, magenta, and yellow gun colors to the Seasat and SPOT channels. Combinations such as XS2-Seasat-XS3 or XS2-XS3Seasat improve enhancement of the hydrological network and of the agricultural parcels, both features depending on the basement structure. The numerical procedures include principal components analysis (PCA) and IHS transforms. The PCA appears to be the best numerical procedure to merge Seasat and SPOT data, if centered and standardized data are used as input files. When using the IHS merging procedure, the best results were obtained after a weighting of Seasat by the XS3

Address correspondence to Herve Y6sou, GSTS / LTGS, Institut de G6ologie, 1 rue Blessig, 67084 Strasbourg Cedex, France. Received 1 March 1991; revised 15 November 1991. 0034-4257 / 93 / $6.00 ©Elsevier Science Publishing Co. Inc., 1993 655 Avenue of the Americas, New York, NY 10010

SPOT channel. However, results obtained when using IHS transform show less enhancement than when using PCA. Lineament maps were derived from the merged color composites. The geometry of the south armorican shear zone is defined. This geometry corresponds to kilometric C surfaces and S planes. The merging of Seasat and SPOT also points out circular features which correspond to known or previously unmapped granites. INTRODUCTION The merging of radar and multispectral data is becoming of greater importance. In the near future, at least three satellites carrying microwave sensors will be launched: the European ERS-1, the Canadian Radarsat, and the Japanese J-ERS-1. Facing this increase in data, it is necessary to combine images from different sensors to reduce processing time and to improve the content of available information. The aim of this article is to present the results on the combinations of Seasat and SPOT data during research conducted at the remote sensing scientific group of Strasbourg (GSTS). There have been many studies investigating

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radar data for geological mapping (e.g., Ford, 1980; Farr, 1984; Sabins, 1983). Some of them have pointed out the benefits of merging radar with visible/near infrared data (Wong and Orth, 1980; Daily et al., 1979), but, here, the purpose was mostly to provide information by increasing the apparent spatial resolution of multispectral data. The aim of the present article is to address methods for merging Seasat and SPOT data in order to create enhanced color composite images. It is hypothesized that the combination of a Seasat radar with visible / near infrared SPOT image will improve structural pattern enhancement over the use of either Seasat data or SPOT data alone. The radar signal of the Seasat data depends mostly on three factors: moisture, topography (slope effect on the backscattering), and surface roughness, the latter being of lesser importance in this temperate region. The visible/infrared data of the SPOT channels provide information on vegetation and on land use, and these features can be related to basement structures (Braux et al., 1990; Rolet and Y6sou, 1990). To a lesser degree, from SPOT data, topographical indications can also be ascertained mostly from shadowing effects. Merging Seasat and SPOT data could thus combine all these types of information. Two different methods of integration were used: photographic techniques and numerical procedures, including principal components analysis (PCA) and intensity-hue-saturation transform (IHS) methods. Geological features derived from Seasat or SPOT data and from merged images are presented. Comparisons have also been made with previous studies so that the accuracy and the advantages of information derived from merged Seasat/SPOT images are pointed out. Two criteria guided the choice of the study area: the need for an overlapping of Seasat and SPOT data over a temperate region and the availability of an accurate ground control survey. The south armorican shear zone region (West Brittany, France) provides both: Seasat and SPOT data were available, and, recently, field work and studies based on satellite images processing have been conducted over this area (Gouronnec et al., 1986; Jouvin, 1986; Rosselo and Le Corre, 1989; Y6sou and Rolet, 1990; Rolet and Y6sou, 1990; Braux et al., 1990).

GEOGRAPHICAL AND GEOLOGICAL SETTING The study area corresponds to an erosional palaeo-surface with low topographic amplitude. The bedrock is hidden by alterites (weathered crystalline and sedimentary rocks) and Quaternary formations. The ancient agricultural system has made Brittany a region intensively crisscrossed by hedges delimiting small ordered fields, woodlands, hamlets, and villages. In such countryside, changes in the parcel arrangements reflect relationships between land use and geological pattern (Y~sou and Rolet, 1990; Braux et al., 1990). In the landscape, the south armorican shear zone (SASZ) appears as a succession of narrow valleys and elongated depressions where some wetlands and ponds occur. The Armorican Massif is dissected by several major late Carboniferous shear zones which subdivide it into distinct zones. These are characterized by their own lithotectonic piles. Four different orogenic periods can be distinguished: the Cadomian orogeny, the Cambrian-Ordovician distensive events with the south armorican and central Massif ocean opening, the Eohercynian (SiluroDevonian) corresponding to ocean closure and to the collisional stage, and the Hercynian (Carboniferous), intracontinental and hypercollisional stages (Rolet, 1991). On the western part of the Armorican Massif, three units can be easily distinguished from north to south (Figs. 1 and 2). The Cadomian domain (CD), situated north of the north armorican shear zone (NASZ), corresponds to the Phanerozoic basement, more or less involved in the variscan events. This domain lies over the studied area. The central armorican domain (CAD) lies between the north and the south armorican shear zones. The CAD shows upper Precambrian basement overlaid by detritic Palaeozoic formations, with intrusive orthogneiss, such as the Douarnenez trondhjemite, tonalite, and Ordovician extensional volcanism (Fig. 2). The main structural features, thin skin and transcurrent tectonics, are dated from upper De-

Merging Seasat and SPOT Imagery 267

The south armorican domain (SAD) and the L~on domain (LD), corresponding to the inner parts of the Eohercynian mobile belt, consist of Palaeozoic tectonic nappes, mainly composed of orthogneiss and micaschists, and stacked during the Siluro-Devonian or Carboniferous periods (Rolet, 1991).

Figure 1. Location of Seasat (1) and SPOT (2) scenes; 3) subseene used for the PCA procedure. The main geological domains and shear zones are: (CD) Cadomian domain, (CAD) central armorican domain, (SAD) south armorican domain, (LN) L6on domain (the dashed areas correspond to the internal zones of the variscan belt), (SASZ) south armorican shear zone, (NASZ) north armorican shear zone. (Br), Brest, (Dz) Douarnenez, (Q) Quimper, (Lr) Lorient.

vonian to lower Carboniferous. These led to a major crustal over thickening and are accompanied by many granitic intrusions (Rolet et al., 1986; Barri~re et al., 1983).

Figure 2. Geologic map of the study area (from Rolet, unpublished data). 1) Tr6m6oc micaschists; 2) Trunvel white micaschists; 3) Tr6ogat prasinites; 4) Ty Lann ultrabasites; 5) Penhors micaschists; 6) Brioverian weakly metamorphized formations; 7) Ordovician to Devonian sedimentary formations; 8) Carboniferous basins; 9) Douarnenez Trondhjemite; 10) Mo~lan orthogneiss; 11) Quimperl6 orthogneiss; 12) Pors Poulhan porphyroidie granite; 13) Tr~gunc granite; 14) undifferentiated leucogranites; 15) leucogranites with abundant migmatical enclaves; 16) mylonitic shear zones, 17) graben.

The SASZ, trending N90-100°E, was active for over 360 My. Initially it corresponded to a left lateral shear zone (Brun and Burg, 1982), then functioned as a dextral transcurrent faulting system, characterized by the development of early ductile mylonite and then later brittle breccias. A 40 km dextral offset has been proposed for this system (J6gouzo and Rosselo, 1988). During the Carboniferous active regional shear, along the SASZ, the central and south domains were intruded by aluminous leucogranites, 345-290 My. Reactivations of the SASZ occurred during late Carboniferous (Stephanian), Tertiary, and Quaternary. Seismic data show that it is still an active zone.

DATA AND PREPROCESSING STEP

A Seasat image and a SPOT multispectral data were used. Their characteristics are presented in

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Table 1. Characteristics of the Seasat and SPOT Images

Scene Date

Seasat

SPOT

070806-0785 08 / 20 / 1978

27-253 04 / 22 / 1987 XS1 = 0.50-0.59/tm XS2 = 0.61-0.68/tm XS3 = 0.79-0.89/~m 23°L

L = 23.5 cm Incidence angle

23 °

Table 1. Images are shown in Figures 3 and 4. First, the geometric correction of SPOT data, to UTM coordinates, was done using a Dipix Aries II system. In order to enhance linear features, directional and high-pass filters were also applied; for these procedures, results were published in previous articles (Y6sou and Rolet, 1990; Rolet and Y~sou, 1990). The second step was performed, on a Sun-IV workstation, using an Erdas image processing software package. To simplify processing and storage, the Seasat image was converted from 16 bytes to 8 bytes. Then different methods of speckle reduction were tested: low-pass (eq median and mean) and adaptive filters of various sizes. The best results were obtained using a modified version of the Sigma filter (Lee, 1983). For this

Figure 4. Seasat subscene after speckle reduction and coregistration to SPOT data. The hydrographic network is the most visible feature on the inland part of the image. The field arrangement is not easy to analyze. The saturation on the coast implied some difficulties to locate the ground control points.

adaptive box filtering algorithm, we use the local standard deviation of the pixels within a kernel surrounding each pixel in order to detect "error" pixels. Since the speckle noise is multiplicative in

Figure 3. Near infrared XS3 SPOT channel, this area corresponds to a 1024 × 800 pixels subseene. The south armorican shear zone is well delimited, corresponds to the axial valley, and is marked in the western part by the Stephanian coal basin. The agricultural parceling is very noticable.

Merging Seasat and SPOT Imagery 269

nature, a pixel is considered to be an "error pixel" if it deviates from the box mean more than 2 standard deviations. Then, it is replaced by an average of those pixels within the kernel that have their intensities within 2 standard deviations. Such iterations were performed twice for kernels of 7 × 7 pixels. The Seasat image was then coregistered to the SPOT data and resampled to 20 m. We chose to use the SPOT data and resampled to 20 m. We chose to use the SPOT image as a master because of the good geometric accuracy and the relative easiness to locate GCPs (B~gni et al., 1988). Even if the Seasat image had a better spatial resolution (12.5 m for this Seasat image by DLR processing compared to 20 m for multispectral SPOT), it presented difficulties for the spatial location of the GCPs. Many of these were taken on the coast line, at road crossings or bridges. On the Seasat image, their identification was also difficult because of, on the one hand, the limited grey level range over the land area and, on the other hand, a saturation by high backscattering on the coast line due to the crashing of waves. The correction

Figure 5. Merged Seasat and SPOT data using photographic techniques. This composite corresponds to XS2, XS3, and Seasat, respectively, in cyan, magenta, and yellow.The hydrographical network is enhanced; it appears in bright orange color. Forests and moorlands are pointed out in a darker orange. On the southern coast line, red color variations are due to different sea level on the acquisition day of Seasat and SPOT data.

was performed using 32 GCPs. The residual mean square error (rms) was less than 1.5 Seasat pixel, less than a SPOT pixel. The major source of error is attributable to the difficulty in defining the location of GCPs in image space. Similar remarks concerning microwave data were made by Welch and Elhers (1988). After coregistration, a shadow appears along the coast because of the variation of sea level between acquisition times. This was more obvious on the south coast (Fig. 5). A similar p h e n o m e n o n also occurred around the inland wetlands: some ponds show different water levels and, consequently, different geometries. From Seasat and SPOT preprocessed data, high resolution negative films were produced. Then color prints were enlarged at a scale of 1 / 100,000 and 1 / 200,000.

PHOTOGRAPHIC MERGING TECHNIQUES Photographic techniques of merging are fairly simple. During the color printing, one of the three

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color planes (cyan, magenta, yellow) is assigned to the Seasat image and the two others to two SPOT channels. This is not a new technique. In order to increase the 80 m spatial resolution of Landsat MSS data, a Seasat image (classical 25 m of spatial resolution) was combined with it, on a video monitor (Daily et al., 1979; Wong and Orth, 1980). More recently, Royer et al. (1984), using aircraft radar data, combined radar and multispectral images this way. In order to be performed successfully, this technique requires a fairly good coregistration of the data set. The rms error obtained in this study for Seasat geometric correction, less than a SPOT pixel, is considered acceptable. Before printing, the 18 possible combinations were performed on a video monitor. Four were selected for color enlargement prints based on criteria such as image saturation or enhancement of the structural features or of the hydrological network. Two combinations showed an improved enhancement of the hydrological network with a moderate saturation. These were XS2-SeasatXS3 and XS2-XS3-Seasat, respectively, on cyan, magenta, and yellow. On these combinations the agricultural parceling and the hydrological network are both very easily perceived while the former was poorly visible on the Seasat data and the latter was poorly visible on the SPOT data (Fig. 5).

NUMERICAL MERGING PROCEDURES In order to numerically merge Seasat image and SPOT channels, we tested different procedures: linear transforms of the data set, principal components analysis (PCA), and intensity-hue-saturation transforms (IHS). Linear transforms gave very poor results; thus we only present here the PCA and IHS procedures.

Principal Components Analysis Principal Components Analysis is a commonly used technique for remote sensing image analysis. It has been used for data enhancement (Soha and Schwartz, 1978; Kahle et al., 1980; Gillespie et al., 1986), as a data compression technique (Chavez and Kwarteng, 1989), and to detect changes in land cover (Fung and Ledrew, 1987; Byrne et al., 1980; Richards, 1984).

Table 2. Statistical Values of the 512 x 512 Pixels Subscene Used for the Principal C o m p o n e n t s Analysis

Seasat XS1 XS2 XS3

Min-Max

Mean

Standard Deviation

7-150 26-103 16-96 9-143

26.71 37.37 27.94 69.63

10.26 4.84 7.40 22.89

PCA is a statistical technique that transforms a multivariate data set of intercorrelated variables into a data set consisting of new uncorrelated linear combinations of the original variables. Thus, PCA generates a new set of axes that are orthogonal to each other and called principal components (PCs). The sum of the variance of the generated PCs is equal to the total variance of the initial variables. Each successive PC explains decreasing variance levels. In this article, we have used the PCA for merging microwaves and multispectral data using the Seasat band and the three SPOT channels as input data. PCA was performed over a representative 512 x 512 pixel zone (location on Figure 1, statistical values in Table 2). The PCA was performed on centered and normalized values (standard deviation = 1) of the fourth input channels. The linear correlations between them are given in the Table 3. The first three principal components explain 98% of the variance; the fourth component has small but significant eigenvalue (2.38%) (Table 4). We notice that CP1 is highly correlated with XS1 and XS2, that CP2 is negatively correlated with the radar band and positively with XS3, and that CP3 is correlated both with XS3 and Seasat (Table 4). Consequently, on the first three components, interesting features can be pointed out: PC1 appears to be a measure of brightness in the two visible SPOT bands (Fig. 6); PC2, expresses differences between Seasat and XS3 SPOT channel, and appears as a merging of these two bands (Fig. 7). The PC3 presents positive loadings of Seasat and XS3 data (Fig. 8). The radar signal has a predominant effect on this component since the Table 3. P e a r s o n C o r r e l a t i o n C o e f f i c i e n t s C o m p u t e d for the Four Input Channels of the ACP

Seasat XS1 XS2

XS1

XS2

XS3

- 0.08

- 0.06 0.89

- 0.02 - 0.06 - 0.02

Merging Seasat and SPOT Imagery 271

Table 4. PCA Results: Eigenvaluesfor the Computed Principal Components and Pearson Correlation Coefficientsbetween the PCs and the Input Channels PC1

PC2

PC3

PC4 2.38

Eigenvalues

48.43

25.60

23.59

Seasat

- 0.13

- 0.78

0.61

0.00

0.95

0.10

0.20

- 0.21

XS2

0.97

- 0.02

0.08

0.22

XS3

- 0.27

0.64

0.72

0.04

XS1

hydrological network is enhanced and appears with high DN values. However, compared with the input Seasat image, this third component presents an enhancement of the agricultural parceling corresponding to the contribution of the XS3 channel. On the PC4 some information appears: spatial patterns such as roads and villages are very easily detectable; they appear with low DN values (Fig. 9). Significant enhancement improvements of merging Seasat and SPOT could be observed on a color display of the three first PCs, respectively, in red, green, and blue (Fig. 10). Interpretation and extraction of information is easier than on a Seasat image or on a SPOT color composite alone. The hydrological network is greatly enhanced; it appears in dark blue and black over a bright landscape (Fig. 10). In addition, on this PC color composite, wetlands and highly moist soils are enhanced, and some present an anomalous distribution that could be related to structural base-

Figure 6. First PC computed using the centered and stan-

dardized Seasat and SPOT data. This channel is similar to the SPOT XS2 or XS1 input data. It corresponds to a data compression of the two visible SPOT channels.

ment patterns. The agricultural parceling is also well preserved, so that the geometric aspect of the input SPOT data is kept. We must, however, notice that when merging SPOT and Seasat data based on a PCA approach, the control on the physical signal is lost, so that this technique cannot be used for all thematic studies.

IHS Transform Methods The IHS color transform is a standard procedure in image analysis. It was successfully used for color enhancement of highly correlated data (Gillespie et al., 1986), for geological features enhancement (Daily, 1983), for combining multispectral data in order to increase the apparent spatial resolution (Baker and Henderson, 1988; Carper et al., 1990; Welch and Ehlers, 1987) and, more recently, to merge geophysical or geochemical data with radar images (Harris et al., 1990). IHS refers to human color perception parame-

Figure 7. Second PC computed using the centered and

standardized Seasat and SPOT data. Features from both Seasat and SPOT XS3 channel are present. The parcel arrangement is preserved from the SPOT channel.

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Figure 8. Third PC computed using the centered and stan-

dardized Seasat and SPOT data. On this image the weight of Seasat data is obvious. This image provides a good example of the successful merging of Seasat and SPOT data. The hydrologicalnetwork is pointed out in very bright tone. Some information provided by the SPOT XS3 channel, such as the fields arrangement, are present in this PC3. ters. The intensity (I) corresponds to the total brightness of a color, the hue (H) to the dominant wavelength contributing to a color, and the saturation (S) specifies the purity of a color (Carper et al., 1990; Gillespie et al., 1986). For merging images, the IHS transform can

Figure 9. Fourth PC computed using the centered and

standardized Seasat and SPOT data. This channel contains mostly random noise. However, some features are better pointed out than on the other PCs. Roads and villages appear in dark color and are easily extractable.

be used in two ways: The first one consists in using different input images to modulate the display of I, H, S: for example, the highest resolution image, such as Sir-B or SPOT panchromatic data, is used to modulate the intensity whereas Landsat Thematic Mapper bands are involved to modulate the hue and saturation parameters (Jaskolla et al., 1985). Also, some authors advise modulating the saturation, and not the intensity, by the higher spatial resolution image (Welch and Ehlers, 1988). The second method consists in calculating the I, H, and S parameters for a set of three elements, which means coding the RGB display of an image in spherical or circular coordinates in the IHS color space. Usually, the merging of a fourth channel (i.e., highest resolution or microwave data) is realized by using this one as the substitute for the computed intensity (Welch and Elhers, 1987; Taranik, 1988). Then a reverse transform to RGB color space is performed. The resulting RGB composite image shows the combined information. Due to software availability, only this second approach o f l H S transform was used. From the three SPOT channels, IHS parameters were calculated. Then the Seasat band was used to substitute the c o m p u t e d intensity, and a reverse transform procedure was finally applied. In such a merging procedure, the weight given to the substitute is crucial. There is a large difference between radar data on the one hand and the visible or near infrared on the other. During the reverse transform, when a raw radar image is used as the intensity substitute, much spatial information (as the parceling arrangement) provided by the input SPOT data is blurred, and the hydrographic network from the Seasat image is greatly enhanced but also enlarged. The weight accorded to Seasat data is obviously too high. Thus we propose to weigh the Seasat data by summing it with a SPOT channel. It was decided to add the XS3 channel because it provides the best information about the parceling arrangement. We tested summing raw, weighed, and also centered and standardized Seasat XS3 data. Centered and standardized ones produced bad results. The best results were obtained with weighed data using the following formula: [0 *Seasat + fl*XS3 +/~] Different values of 0 and fl were checked (0 = 1 or 2, fl = 1, 1.5, 2, 3;/t is a scaling factor that we

Merging Seasat and SPOT Imagery 273

Figure 10. Color composite of the first three PCs computed using the centered and standardizedSeasat and SPOT data (PC1, PC2, and PC3 respectively, on red, green, and blue). This subscene covered 512 × 400 pixels. The hydrological network appears in dark blue or black colors. Fields, preserved during the Seasat and SPOT merging, are shown in a bright color.

have assigned to the difference between the mean of XS3 and that of Seasat). The use of this complex substitute replacing the computed intensity produces good results, better than those using the raw radar data. The best IHS merging was obtained when using the following parameters values: ~ = 1, fl= 3. On the combined output data set, the agricultural parceling is preserved. It is not as distinct as on the SPOT image, but better than when using the raw radar as a substitute. Hydrographical network and also sea pattern are greatly enhanced.

RESULTS AND DISCUSSION Image Analysis Using color print enlargements of Seasat, SPOT, and also the results of photographic and numerical merging procedures, linear and circular features were visually extracted and drawn onto mylar overlays (Figs. 11-14). Using geographical maps for comparison, all man-made features such as roads, highways, and airports were rejected. Based on field control survey and on previous studies (Barri~re, 1970; Jouvin, 1986; Gouronnec et al., 1986, Y6sou and Rolet, 1990; Rolet and Y6sou, 1990; Braux et al., 1990), the four following basic families of linear elements could be discerned. The first one, oriented N50-70°E, corre-

sponds to a regional foliation defined in the crystalline rocks (western area), and to a regional cleavage in the sedimentary Palaeozoic and Proterozoic series (eastern area). The foliation trend varies from north to south, coming closer to N70°E, in the axial part of the studied area, near the south armorican shear zone. The foliation is well marked by the alignment of field edges, and it controls the microtopography. On the Seasat image, this network is slightly visible; the Seasat data does not provide us with a lot of information about the spatial organisation of the parceling. On the Seasat image, the 5070°E foliation was most apparent and was mapped principally on the NE part of the studied area (2 in Fig. 11). On the SPOT image, the landscape parceling is the most important feature due to the fine spatial resolution of SPOT sensors. The parcel arrangement was easily extracted, and a precise map of the foliation prepared (1 in Fig. 12). On the merged data, either photographic or PC composite images, this 50-70°E system is well expressed, both in the NE part and near the SASZ. A precise and fine map of the foliation was derived from these integrated image prints (4 in Fig. 13 and 4 in Fig, 14). The second one, a N90-105°E family, corresponds to mylonite shear zones. It is the major feature of the study area. The most important one is located in the axial part of the studied area and corresponds to the south armorican shear zone.

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/

3

15km

I

Figure 11. Lineament map derived from Seasat data: 1) N90-105°E network; 2) faint N5070°E patterns; 3) NS and N150°E structures well marked by the hydrographical network; 4) circular feature.

Repeats of this occurrence are developed on the southern border of the SASZ. The EW shear system is mainly marked on the landscape by the major hydrographic network located on the axial zone of the studied area. On the Seasat image, this geomorphological pattern is highly enhanced, appearing on the image in bright tones (Fig. 4). Seasat data provides a good mapping tool for this EW system (1 in Fig. 11). On the SPOT data, the EW system appears in dark tones, and locally presents some difficulties to be extracted (Fig. 3). On the color Seasat / SPOT merged image, the Seasat enhancement and the fine spatial resolution of SPOT are combined, so that the N90-105°E system is very well documented; the geometry of the SASZ is pointed out better than on the SPOT data (1 in Fig. 13). Two later fracture systems could also be distinguished, an NS and an N150°E, respectively

late-Variscan and post-Lias in age. The NS and N150°E structures are expressed by elements of the hydrographical network and also by changes in the arrangement of fields. On the Seasat image, the two systems are easily distinguished on the north coast, because, in this area, it is elucidated on the topography by small valleys (3 in Fig. 11). In the SPOT image, these systems appear mostly in the variation of the field arrangement and, to a lesser degree, as it is less perceptable, by hydrological features. The NS and N150°E are highly enhanced on this Seasat/SPOT color composite compared to the SPOT image alone. A precise mapping of the NS and N150°E systems was done using the colormerged composite images, either from the results of the photographic techniques or the color composite of the first three PCs (2 in Fig. 13 and 3 in Fig. 14). Curvilinear and circular elements were also

Merging Seasat and SPOT Imagery 275

15km

f

Figure 12. Lineament map derived from SPOT data: 1) dense N50-70°E network; 2) N90105°E system corresponding to the south armorican shear zone; 3) NS and N150°E structures; 4) circular features corresponding to the Locronan leucogranite.

extracted. These might indicate plutonic domes. Some of these correspond to well-known and previously mapped granites, others to unknown structures, but, in comparison with regional geology, these can be interpreted as hidden plutonic domes. For the previously mapped granites, their geometry and extension were more precisely indicated (Y~sou et al., 1991). Circular features mostly appear in the radial disposition of cultivated fields. The best information is therefore provided by the visual interpretation of landscape units. The SPOT color composite gives an important amount of information. For example, the geometry of the Locronan leucogranite is very well documented (4 in Fig. 12). On the Seasat image, circular features are present but in lesser numbers than on the SPOT image. Also, their geometries are often incomplete; quite often only part of the granite structure detected on the SPOT image is

visible. This could be related to the fact that a lot of circular structures are revealed or enhanced by field arrangement, and, on the Seasat image, the agricultural parceling is not easily distinguishable. On the merged Seasat/SPOT color composite images, circular features are documented as often as on the SPOT image alone. In the northeast area, the geometry of the Locronan leucogranite is clearly pointed out; precise patterns such as the late fractures crossing it are mapped (3 in Fig. 13). Similar remarks can be made for the circular structure located in the center of the image (Fig. 14). In the south area, a circular structure extending over several square kilometers, not previously mapped in either the Seasat or SPOT alone, was best described on the photographically merged Seasat / SPOT image (4 in Fig. 13). These last examples prove the interest and benefits of merging Seasat and SPOT data.

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2

15km

Figure 13. Lineament map prepared from Seasat/SPOT color composite image obtained using the photographic procedure: 1) well-documented N90-105°E network, and EW repeats, providing a very good example of the improvement by Seasat and SPOT merging; 2) NS and N150°E late fracture network; 3) Locronan leucogranite cross-cut by late fracturing; 4) previously unmapped circular structure.

DISCUSSION

SPOT image analysis provides a lot of information, mostly because of the spatial resolution of the sensors and their sensibility for detecting vegetation, but it represents a great amount of data to be processed and interpreted (Table 5). Using only one Seasat image, we know that part of the information, situated in + 15 ° of the scene illumination direction, is lost (Ford, 1980). Unfortunately, only one Seasat image is available over the study area; it was not possible to limit this illumination effect. With the launch of ERS-1, it will be possible to counterbalance this SAR effect by using the dual direction of scene illumination. On the Seasat image, topographic and moisture features are enhanced, in comparison to the

SPOT image. However, only bare moist parcels are pointed out by the radar data. In addition, it reinforces the hydrological network image. Care must be taken in the interpretation of these features because, locally, only some trends of the basement structures are represented. The hydrographical network greatly enhanced by the radar is controlled by Palaeozoic structures reactived during the Quaternary Period. The structural sketch derived from Seasat represents the recently reactived structures of the basement rather than the original ones. On the combined Seasat and SPOT images, merged using either photographic techniques or PCA procedures, lineaments are more readily extractable and interpretable than when using Seasat or SPOT images alone. The south armorican shear zone and the NS and N150°E late fracturing

Merging Seasat and SPOT Imagery 277

CONCLUSION

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t

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I

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This study shows the utility of remotely sensed data for structural cartography over temperate and agricultural regions. Using different procedures, photographic techniques, and numerical procedures, we succeeded in merging the information contained in Seasat and SPOT data. There are two merging procedures providing very good results: photographic merging techniques and PCA.

I

Figure 14. L i n e a m e n t m a p d e r i v e d f r o m t h e c o l o r c o m p o s i t e o f t h e first t h r e e P C s c o m p u t e d u s i n g t h e c e n t e r e d a n d s t a n d a r d i z e d S e a s a t a n d S P O T data: 1,2) e l e m e n t s o f t h e s o u t h a r m o r i c a n s h e a r z o n e ; 3) NS s t r u c t u r e s ; 4) N 5 0 - 7 0 ° E d e n s e n e t w o r k c o r r e s p o n d i n g to a r e g i o n a l foliation.

systems are well-documented structures. The Seasat and SPOT merging is also of a great interest for mapping the granitic domes (Table 5). This study confirms our previous interpretation of the south armorican shear zone (Rolet and Y6sou, 1990). The SASZ presents a geometrical organization of kilometric S surfaces (schistosity) and C planes (shear plane); such models of shear zones are well described at centimetric or smaller scales (J6gouzo, 1980). This fracturing corresponds well to a dextral shear zone. The S surfaces and C planes can be mapped both from the Seasat and SPOT data, but are brightly enhanced both on the photographic Seasat/SPOT color composite and on the principal component composite as well.

The photographic merging techniques are easily realizable, although a good coregistration is required. The color composites, XS2-Seasat-XS3 or XS2-XS3-Seasat, respectively, in cyan, magenta, and yellow, offer an improved enhancement of the hydrological network. The agricultural parceling, mostly present on the XS SPOT channels and poorly represented on the Seasat data, are also easy to see on the combined image. PCA appears to be the best numerical procedure for merging Seasat and SPOT data. Interpretation and extraction of information is easier than on raw SPOT or raw Seasat data and as easy as on SPOT/Seasat color composite. On the PC color composite the hydrological network is greatly enhanced. In addition, wetlands and highly moist soils are pointed out, and some present an anomalous distribution that could be related to structural basement patterns. A lot of authors advise using the IHS transform to merge multispectral data; however, for this site, the results obtained using IHS transform

Table 5. G e o l o g i c a l I n f o r m a t i o n E x t r a c t e d f r o m R a w a n d M e r g e d S e a s a t and SPOT Images" N50-70°E, Geom-Par Seasat SPOT XS2-SEA-XS3 XS2-XS3-SEA IHS ACP

+ +++ + + + + + ++

N90-150°E, NS N150°E Geom-Hyd Hyd-Par Hyd-Par ++ + ++ + + + + + + + + +++

++ ++ + + + + + + + +++

++ ++ + + + + + + + + + +++

Circular Par + + + + + + +/+ + + + +/+ + -+++

a Feature-enhancing geological structures are: (Geom) topographic variations, (Hyd) hydrographical network, (Par) field arrangement.

278 Y~sou et al.

show less enhancement than when using the PCA procedure. From the processed images, a great amount of geological structures were pointed out. This study confirms that the organization of the south armorican shear zone corresponds to kilometric C surfaces and S planes. The merging of Seasat and SPOT data also provides a great enhancement of circular features. These structures might indicate plutonic domes. Some of these features correspond to well-known and previously mapped granites, others to unknown structures that, referring to regional geology, can be interpreted as hidden plutonic domes. Hence, they can represent potential mineral exploration targets. This research has been supported by the PNTS-1990 (Programme National de Te'l~d~tection Spatiale) directed by the CNRS and CNES. The authors would like to thank M. A. L'Hyver and I. Lund~n who kindly read and commented on this article and also P. de Fraipont and C. Bestault of the SERTIT (Service R~gional de Traitement d'Image et de T~l~ddtection of Strasbourg) and R. Saint Jean of the Sherbrooke University (Quebec, Canada) for their help.

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