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Preliminary results of the comparative analysis of ERS-1 and ALMAZ-1 SAR data
12
ISPRS Journal oJ Photogrammetry and Remote Sensing
Karl-Heinz Marek i and Karsten Schmidt
Preliminary results of the comparative analysis of ERS-1 and ALMAZ-1 SAR data This paper deals with a comparative analysis of ERS-I and ALMAZ-1 SAR data from an urban area test site. T h e investigation includes a visual thematic interpretation using recognition criteria for topographic objects and makes a statistical analysis based on parameters featuring the information content. Classification of the S A R data sets have been performed to investigate the separability of topographic objects for each SAR system as well as for the combination of both data sets.
1. Problem Whereas publications and meetings have been concerned intensively with the performance and results of the European Earth Remote Sensing Satellite ERS-1, similar investigations for the Russian SAR system ALMAZ-1, which has been in orbit since March 1991, have not been carried out so far. ALMAZ-1 data and those of its predecessor ALMAZ, which was in orbit on the space platform KOSMOS 1870 between 1987 and 1989, were referred to as the first commercially available SAR data. According to schedule, ALMAZ-1 ceased to operate in the beginning of November 1992; the follow-up system is in preparation. Land applications of ERS-1 SAR data published so far have been mainly concerned with large area vegetation inventories (Lichtenegger, 1992). This is the first report of a comparative analysis based on statistical and thematical evaluation of the information content of each system and the composite data set. 2. Test site and input information The size of the test area is about 10 × 25 km 2 and is situated in Berlin. It contains nearly all the land-use categories which are typical for an urban geosystem, e.g., densely built-up inner city l uve Potsdam G m b H , Fernerkundungszentrum, Berliner Str. 50, O-1560 Potsdam, Germany.
areas, loosely built-up suburban areas, industrial and manufacturing areas, infrastructure, forest and park areas, water, etc. The test site is flat so the deviations from a presumed mean altitude level are less than +10 m. The following input information was used for analysis: ALMAZ-1 scene of June 17, 1991 (product level B, radiometrically and geometrically corrected, not geocoded); ERS-1 scene of August 12, 1991 (product level, SAR.PRI, not geocoded); Landsat Thematic Mapper scene of July 27, 1990; CIR aerial photographs; and meteorological data during acquisition time. The topographic map of Berlin at the scale of 1:100,000 (TK 100) served as the geometric reference base. The evaluation of urban ecological units was made using the Umweltatlas Berlin (Environment Atlas of Berlin, Der Senator f/Jr Stadtentwicklung, 1985). 3. System parameters and preprocessing
3.1. Comparison of system parameters The juxtaposition of technical parameters of both systems ALMAZ-1 and ERS-1 (Table 1) shows clearly the difference in parameters concerned with image quality and information content (ALMAZ-1, 1991; ESA, 1992). The incidence angle and the wavelength of the system are most important for the backscattering of radar waves by objects. Direction of flight and polarization of both systems (VV for ERS-1, HH for ALMAZ-1), however, are of minor importance
1SPRS Journal of Photogrammetry and Remote Sensing, 49(3): 12-18 0924-2716/94/$07.00 © 1994 Elsevier Science B.V. All rights reserved.
Volume 49, number 3, 1994
13
Table 1 Comparison of technical parameters
Flight altitude (km) Orbital inclination Wavelength (cm) Polarization Incidence angle Spatial resolution range (m) azimuth (m) Radiometric resolution (bits per pixel) Scene size (km 2) Geographic limitation Coverage cycle (days)
ALMAZ-1
ERS-1
280 73° 10 (S-Band) HH 30-60*
783 98* 5.6 (C-Band) VV 23*
15-30 15
<33 <30
8 40 x 40 78"N-78°S 5-7
16 100 x 100 none 3, 35, 176
for backscattering. It should be mentioned that every data point recorded by the ERS-1 represents the average taken from three looks while the corresponding ALMAZ-1 data point represents just one single look.
3.2. Preprocessing of SAR data For the comparison of SAR images from different sensor systems, it is necessary to carry out a number of geometric and radiometric procedures. These include: removal of interference phenomena (speckle effect); histogram matching based on a selected grey level distribution; registration on a topographic data base; and data registration on a digital terrain model (DTM). Due to the abovementioned slight terrain differences within the test site, the data correction with a DTM could be neglected. For removing the speckle effect, a gaussian filtering procedure in the frequency domain was carried out (Cappellini et al., 1978). The optimum cut-off frequencies in the x- and y-directions were determined by trial and error. Matching both radar systems radiometrically, a data compression from the REAL format (as a result of the FFT) to the BYTE respectively IMAGE format was accomplished by using an equal probability distribution. This consideration was based on the assumption that every grey level coded backscatter signal should have equal probability. The geometric registration of each radar image on the TK 100 was carried out using 25 ground control points (GCP) distributed all over the test
area. To prevent the filtered grey level values from being changed once more, it was reasonable to apply the nearest neighbour resampling method rather than any interpolation procedure. Fig. 1 shows registered sections of both SAR scenes. 4. Results of the comparative analysis of the information content
4.1. Visual image analysis The preprocessed SAR images were analyzed concerning their recognition ability on areal, line and point features. Also, the mutual separability between areal objects and the ability to detect line and point objects on each radar image were evaluated according to a three-level scale. The results are shown in Tables 2-4. Considering the point and line objects, it can be concluded that due to the higher spatial resolution of ALMAZ-1, these features were in general more clearly identified in ALMAZ-1 than in ERS-1 scenes (see Tables 3 and 4). Only the big avenues and dominant single objects (e.g. circus Grosser Stern, Tiergarten) can be detected; the net of second-order streets, however, could not be discerned. A different situation can be observed for the areal objects: the steeper incidence angle of the ERS-1 obviously caused corner reflexions in the street hollows and therefore results in more backscattering over the densely settled inner city areas than in the lesser populated suburban areas. This enables a separation between the basic categories of settlements for the ERS-1 scene. The ALMAZ-1 system, however, shows backscattering for both categories with intensities of equal magnitude, making discrimination impossible (see Table 2). Due to its higher wavelength, ALMAZ-1 penetrates deeper vegetation layers and, according to the different tree morphologies, succeeds in discriminating mixed forest stands (pine and deciduous) in the Tegel area from pure deciduous stands in the Grunewald area. The shorter wavelength of the ERS-1, however, leads to backscattering in the upper canopy layer and hampers the separation between both forest types (see Table 2). The rippling of the water surface caused by wind force leads to masking effects between land and water in the ERS-1 image. If the roughness depth is of the same magnitude as the system's
Figure 1. Registered sections of the ERS-1 (left) and ALMAZ-1 (right) scenes.
~L
Volume 49, number 3, 1994
15
Table 2 Separation matrix (areal objects) ERS-1 (object row), A L M A Z - 1 (object column) Areal objects
Airport
Airport Lake 1 Lake 2 Forest mixed deciduous Suburban area Inner city Industrial area 1 Industrial area 2
Lake 1
Lake 2
+ 0 0
0
++ ++ ++ ++ ++ ++
++ ++ ++ ++ ++ ++
Forest
+ 0
++ ++ ++ ++ ++ ++
Suburban area
Inner city
Industrial area 1
2
mixed
decidous
++ 0/+ +
++ 0/+ +
++ 0/+ +
++ + +
++ + +
++ + +
+
+ 0
++ ++ +
++ ++ + 0
++ ++ 0 0 0
+ + + + +
0 + + +
0/+ + 0/+
+ +
0
0 = not discernible; + = discernible; + + --- good discernible. Table 3 Separation matrix (linear objects) Linear objects
ERS-1
ALMAZ-1
ERS-1/ALMAZ-1
ERS-1/ALMAZ/Texture (ERS-1)
Heerstrasse to Tiergarten Motorway from SW to N N E Second-order streets Teltow channel Spree Hohenzollern channel Teltow channel bridges
Hohenzollern bridges
0
++ + + ++ + ++ + (12 out of 18) + (10 out of 15) + (3 out of 4)
++ + 0/+ ++ + ++ 0/+ (6 out of 18) + (8 out of 15) +
+ 0/+ 0 ++ +/0 ++ 0/+
Spree bridges
++ + 0 + +/0 + 0 (2 out of 18) 0
0/+ (6 out of 15) 0
0 = not discernible; + = discernible; + + = good discernible. Table 4 Separation matrix (point objects) Point objects
ERS-1
ALMAZ-1
ERS-1/ALMAZ-1
ERS- 1 / A L M A Z - 1/texture (ERS-1)
Grosser Stern (circus) E. Reuter square (circus) Funkturm at the fair halls Gasometer Olympic stadium Trotting-race course Marienfelde
+ + + + + +
+ + + + + +
++ ++ ++ + + +
+ + + 0 + +
+ + + + + +
0 = not discernible; + = discernible; + + = good discernible. wavelength, being
the incoming
specularly
scattered
reflected,
waves, instead
are actually
and lead to the rippling
above. This phenomenon eastern
radar
bank
of Lake
effect described
is c l e a r l y o b s e r v e d Tegel where
of
diffusively on the
the land-water
line cannot be distinguished. The
predominant
1 overflight
caused
winds
during
a roughness
the ALMAZpattern
water surface similar to the one encountered ERS-1.
Due
to the higher wavelength
on
the
by the
of ALMAZ-
16
1, however, the incoming radiation was specularly reflected, thus giving no backscatter signal.
4.2. Statistical image analys& The areal objects evaluated in the last section represent typical land-use categories of an urban geosystem whose grey level distributions can be analyzed. For this purpose the information entropy values after Shannon and the variance values of the grey level distribution were computed for all the objects concerned. Whereas the information entropy measures the deviation of an equal probability distribution, the variance indicates the homogeneity of the objects. The results are presented in Figs. 2 and 3. With two exceptions, the entropy and variance values of the object categories are higher for the ALMAZ-1 system than for the ERS-1 system. This result can also be explained as due to a higher spatial resolution and a different incidence angle. The two exceptions concern the following. (1) The above-mentioned ripple effect of the water surface of Lake Tegel: the higher entropy value for the ERS-1 confirms the results already discussed in Sect. 3.1. (2) The different morphology of the industrial/ manufacturing areas (1, 2). Area 1 can be char-
ISPRS Journal o] Photogrammetry and Remote Sensing,
acterized as relatively inhomogeneous owing to its rugged built-up structure, whereas area 2 consists predominantly of flat, homogeneous objects (factory halls with large area roofs) whose roughness results in more diffuse scattering for the ERS-1 with its shorter wavelength than for the ALMAZ-1 with its longer wavelength. This difference between both industrial areas is even more conspicuous in the case of the variance values (Fig. 3). The evaluation of both data sets for mapping applications was performed by unsupervised and supervised classifications. The unsupervised classification or clustering represents mathematically a histogram segmentation of the image whose output depends on the cluster distance selected. The distance of 30 grey levels separates 7 classes in both SAR scenes: the composite of ERS-1 and ALMAZ-1, however, leads to 8 classes even when the cluster distance is trebled (100 grey levels). The object windows selected served also as training sets for a supervised classification of the SAR composite. It could be shown, however, that the two-dimensional histograms of the training sets strongly penetrate each other in the feature space. Therefore, the definition of a discriminance function or separability turned out to be very difficult. The results of the supervised classifica-
Volume 49, number 3, 1994
17
Figure 3. Variance values of ERS-1/ALMAZ-1 selected object windows: I = airport; 2 = lake 1; 3 = lake 2; 4 = forest (M); 5 = forest (D); 6 = suburban area; 7 = innercity;8, 9 = industrialarea. tion with the Maximum-Likelihood procedure did not significantly improve the results obtained by the unsupervised classifications (e.g. MinimumDistance method). The integration of the texturecoded ERS-1 scene in the composite of ERS-1 and ALMAZ-1 increased the separation between areal objects significantly. All the colour composites generated during this work confirmed the statistical evaluation. In addition to the composites of both SAR systems, synergistic data sets between radar and Landsat Thematic Mapper (TM) data were created for comparative purposes. Out of the seven TM bands, three channels in the NIR and MIR were selected and imposed by an IHS transformation. The intensity band was then replaced by either SAR data set. The colour composites ALMAZ-1/ Landsat TM turned out to show more topographic features than the ERS-1/Landsat TM composite (Fig. 4).
olution - - to be more successful in detecting linear structures than ERS-1 data, On the other hand, ERS-1 data generally showed better ability to differentiate areal objects (e.g. separation between densely built-up inner city and loosely built-up suburban areas), whereas ALMAZ-1 data led to better discrimination between different forest stands. The ripple effect of roughened water surfaces (in ERS1) clearly supported the unambiguous separation between land and water. The best results in object recognition were achieved by creating composites of both SAR data systems. In this way the advantages of the multifrequency concept, already verified during extensive airborne SAR campaigns in Europe and elsewhere (Keydel, 1986; Brown et al., 1992) have been confirmed. For improving classification accuracies, the derivation and integration of additional spatial feature levels looks promising. Texture parameters derived from second-order frequency matrices could be valuable tools for that purpose.
4.3. Conclusion
The comparative analysis of the information content of SAR images of an urban area proved ALMAZ-1 data - - due to their higher spatial res-
Acknowledgement The support of the ESA in 1992 is gratefully acknowledged.
18
ISPRS Journal of Photograrnmelty and Remote Sensing
Figure 4. Section of test area. Left: composite of ERS-1 and Landsat-TM. Right: composite of ALMAZ-1 and Landsat-TM.
References ALMAZ-I, 1991. Product Specification Note. Hughes JTX Corporation. Brown, R.J., Manore, M.J. and Poirier, S., 1992. Correlations between X-, C-, and L-band imagery within an agricultural environment. Int. J. Remote Sensing, 13 (9): 16451661. Cappellini, V., Constantinides, A.G. and Emiliani, E, 1978. Digital Filters and their Applications. Academic Press, London, 393 pp. ESA, 1992. ERS-1 User Handbook. Nordwijk.
Keydel, W., 1986. X-SAR extcnds the frequency range of Shuttle imaging radar. Proc. IGARSS' 86 Symp. ESA, Ztirich, pp. 1207-1212. Lichtenegger, J., 1992. ERS-I: landuse mapping and crop monitoring: a first close look lo SAR data. Earth Observ. Q., 3 7 - 3 8 : 1 - 5 Der Senator for Stadtentwicklung und Umweltschutz des [.andes Berlin, 1985/86. Umweltatlas Berlin. Kulturbuchverlag, Berlin. (Received January 25, 1993; revised and accepted May 16, 1993)
ISPRS Journal oJ Photogrammetry and Remote Sensing
Karl-Heinz Marek i and Karsten Schmidt
Preliminary results of the comparative analysis of ERS-1 and ALMAZ-1 SAR data This paper deals with a comparative analysis of ERS-I and ALMAZ-1 SAR data from an urban area test site. T h e investigation includes a visual thematic interpretation using recognition criteria for topographic objects and makes a statistical analysis based on parameters featuring the information content. Classification of the S A R data sets have been performed to investigate the separability of topographic objects for each SAR system as well as for the combination of both data sets.
1. Problem Whereas publications and meetings have been concerned intensively with the performance and results of the European Earth Remote Sensing Satellite ERS-1, similar investigations for the Russian SAR system ALMAZ-1, which has been in orbit since March 1991, have not been carried out so far. ALMAZ-1 data and those of its predecessor ALMAZ, which was in orbit on the space platform KOSMOS 1870 between 1987 and 1989, were referred to as the first commercially available SAR data. According to schedule, ALMAZ-1 ceased to operate in the beginning of November 1992; the follow-up system is in preparation. Land applications of ERS-1 SAR data published so far have been mainly concerned with large area vegetation inventories (Lichtenegger, 1992). This is the first report of a comparative analysis based on statistical and thematical evaluation of the information content of each system and the composite data set. 2. Test site and input information The size of the test area is about 10 × 25 km 2 and is situated in Berlin. It contains nearly all the land-use categories which are typical for an urban geosystem, e.g., densely built-up inner city l uve Potsdam G m b H , Fernerkundungszentrum, Berliner Str. 50, O-1560 Potsdam, Germany.
areas, loosely built-up suburban areas, industrial and manufacturing areas, infrastructure, forest and park areas, water, etc. The test site is flat so the deviations from a presumed mean altitude level are less than +10 m. The following input information was used for analysis: ALMAZ-1 scene of June 17, 1991 (product level B, radiometrically and geometrically corrected, not geocoded); ERS-1 scene of August 12, 1991 (product level, SAR.PRI, not geocoded); Landsat Thematic Mapper scene of July 27, 1990; CIR aerial photographs; and meteorological data during acquisition time. The topographic map of Berlin at the scale of 1:100,000 (TK 100) served as the geometric reference base. The evaluation of urban ecological units was made using the Umweltatlas Berlin (Environment Atlas of Berlin, Der Senator f/Jr Stadtentwicklung, 1985). 3. System parameters and preprocessing
3.1. Comparison of system parameters The juxtaposition of technical parameters of both systems ALMAZ-1 and ERS-1 (Table 1) shows clearly the difference in parameters concerned with image quality and information content (ALMAZ-1, 1991; ESA, 1992). The incidence angle and the wavelength of the system are most important for the backscattering of radar waves by objects. Direction of flight and polarization of both systems (VV for ERS-1, HH for ALMAZ-1), however, are of minor importance
1SPRS Journal of Photogrammetry and Remote Sensing, 49(3): 12-18 0924-2716/94/$07.00 © 1994 Elsevier Science B.V. All rights reserved.
Volume 49, number 3, 1994
13
Table 1 Comparison of technical parameters
Flight altitude (km) Orbital inclination Wavelength (cm) Polarization Incidence angle Spatial resolution range (m) azimuth (m) Radiometric resolution (bits per pixel) Scene size (km 2) Geographic limitation Coverage cycle (days)
ALMAZ-1
ERS-1
280 73° 10 (S-Band) HH 30-60*
783 98* 5.6 (C-Band) VV 23*
15-30 15
<33 <30
8 40 x 40 78"N-78°S 5-7
16 100 x 100 none 3, 35, 176
for backscattering. It should be mentioned that every data point recorded by the ERS-1 represents the average taken from three looks while the corresponding ALMAZ-1 data point represents just one single look.
3.2. Preprocessing of SAR data For the comparison of SAR images from different sensor systems, it is necessary to carry out a number of geometric and radiometric procedures. These include: removal of interference phenomena (speckle effect); histogram matching based on a selected grey level distribution; registration on a topographic data base; and data registration on a digital terrain model (DTM). Due to the abovementioned slight terrain differences within the test site, the data correction with a DTM could be neglected. For removing the speckle effect, a gaussian filtering procedure in the frequency domain was carried out (Cappellini et al., 1978). The optimum cut-off frequencies in the x- and y-directions were determined by trial and error. Matching both radar systems radiometrically, a data compression from the REAL format (as a result of the FFT) to the BYTE respectively IMAGE format was accomplished by using an equal probability distribution. This consideration was based on the assumption that every grey level coded backscatter signal should have equal probability. The geometric registration of each radar image on the TK 100 was carried out using 25 ground control points (GCP) distributed all over the test
area. To prevent the filtered grey level values from being changed once more, it was reasonable to apply the nearest neighbour resampling method rather than any interpolation procedure. Fig. 1 shows registered sections of both SAR scenes. 4. Results of the comparative analysis of the information content
4.1. Visual image analysis The preprocessed SAR images were analyzed concerning their recognition ability on areal, line and point features. Also, the mutual separability between areal objects and the ability to detect line and point objects on each radar image were evaluated according to a three-level scale. The results are shown in Tables 2-4. Considering the point and line objects, it can be concluded that due to the higher spatial resolution of ALMAZ-1, these features were in general more clearly identified in ALMAZ-1 than in ERS-1 scenes (see Tables 3 and 4). Only the big avenues and dominant single objects (e.g. circus Grosser Stern, Tiergarten) can be detected; the net of second-order streets, however, could not be discerned. A different situation can be observed for the areal objects: the steeper incidence angle of the ERS-1 obviously caused corner reflexions in the street hollows and therefore results in more backscattering over the densely settled inner city areas than in the lesser populated suburban areas. This enables a separation between the basic categories of settlements for the ERS-1 scene. The ALMAZ-1 system, however, shows backscattering for both categories with intensities of equal magnitude, making discrimination impossible (see Table 2). Due to its higher wavelength, ALMAZ-1 penetrates deeper vegetation layers and, according to the different tree morphologies, succeeds in discriminating mixed forest stands (pine and deciduous) in the Tegel area from pure deciduous stands in the Grunewald area. The shorter wavelength of the ERS-1, however, leads to backscattering in the upper canopy layer and hampers the separation between both forest types (see Table 2). The rippling of the water surface caused by wind force leads to masking effects between land and water in the ERS-1 image. If the roughness depth is of the same magnitude as the system's
Figure 1. Registered sections of the ERS-1 (left) and ALMAZ-1 (right) scenes.
~L
Volume 49, number 3, 1994
15
Table 2 Separation matrix (areal objects) ERS-1 (object row), A L M A Z - 1 (object column) Areal objects
Airport
Airport Lake 1 Lake 2 Forest mixed deciduous Suburban area Inner city Industrial area 1 Industrial area 2
Lake 1
Lake 2
+ 0 0
0
++ ++ ++ ++ ++ ++
++ ++ ++ ++ ++ ++
Forest
+ 0
++ ++ ++ ++ ++ ++
Suburban area
Inner city
Industrial area 1
2
mixed
decidous
++ 0/+ +
++ 0/+ +
++ 0/+ +
++ + +
++ + +
++ + +
+
+ 0
++ ++ +
++ ++ + 0
++ ++ 0 0 0
+ + + + +
0 + + +
0/+ + 0/+
+ +
0
0 = not discernible; + = discernible; + + --- good discernible. Table 3 Separation matrix (linear objects) Linear objects
ERS-1
ALMAZ-1
ERS-1/ALMAZ-1
ERS-1/ALMAZ/Texture (ERS-1)
Heerstrasse to Tiergarten Motorway from SW to N N E Second-order streets Teltow channel Spree Hohenzollern channel Teltow channel bridges
Hohenzollern bridges
0
++ + + ++ + ++ + (12 out of 18) + (10 out of 15) + (3 out of 4)
++ + 0/+ ++ + ++ 0/+ (6 out of 18) + (8 out of 15) +
+ 0/+ 0 ++ +/0 ++ 0/+
Spree bridges
++ + 0 + +/0 + 0 (2 out of 18) 0
0/+ (6 out of 15) 0
0 = not discernible; + = discernible; + + = good discernible. Table 4 Separation matrix (point objects) Point objects
ERS-1
ALMAZ-1
ERS-1/ALMAZ-1
ERS- 1 / A L M A Z - 1/texture (ERS-1)
Grosser Stern (circus) E. Reuter square (circus) Funkturm at the fair halls Gasometer Olympic stadium Trotting-race course Marienfelde
+ + + + + +
+ + + + + +
++ ++ ++ + + +
+ + + 0 + +
+ + + + + +
0 = not discernible; + = discernible; + + = good discernible. wavelength, being
the incoming
specularly
scattered
reflected,
waves, instead
are actually
and lead to the rippling
above. This phenomenon eastern
radar
bank
of Lake
effect described
is c l e a r l y o b s e r v e d Tegel where
of
diffusively on the
the land-water
line cannot be distinguished. The
predominant
1 overflight
caused
winds
during
a roughness
the ALMAZpattern
water surface similar to the one encountered ERS-1.
Due
to the higher wavelength
on
the
by the
of ALMAZ-
16
1, however, the incoming radiation was specularly reflected, thus giving no backscatter signal.
4.2. Statistical image analys& The areal objects evaluated in the last section represent typical land-use categories of an urban geosystem whose grey level distributions can be analyzed. For this purpose the information entropy values after Shannon and the variance values of the grey level distribution were computed for all the objects concerned. Whereas the information entropy measures the deviation of an equal probability distribution, the variance indicates the homogeneity of the objects. The results are presented in Figs. 2 and 3. With two exceptions, the entropy and variance values of the object categories are higher for the ALMAZ-1 system than for the ERS-1 system. This result can also be explained as due to a higher spatial resolution and a different incidence angle. The two exceptions concern the following. (1) The above-mentioned ripple effect of the water surface of Lake Tegel: the higher entropy value for the ERS-1 confirms the results already discussed in Sect. 3.1. (2) The different morphology of the industrial/ manufacturing areas (1, 2). Area 1 can be char-
ISPRS Journal o] Photogrammetry and Remote Sensing,
acterized as relatively inhomogeneous owing to its rugged built-up structure, whereas area 2 consists predominantly of flat, homogeneous objects (factory halls with large area roofs) whose roughness results in more diffuse scattering for the ERS-1 with its shorter wavelength than for the ALMAZ-1 with its longer wavelength. This difference between both industrial areas is even more conspicuous in the case of the variance values (Fig. 3). The evaluation of both data sets for mapping applications was performed by unsupervised and supervised classifications. The unsupervised classification or clustering represents mathematically a histogram segmentation of the image whose output depends on the cluster distance selected. The distance of 30 grey levels separates 7 classes in both SAR scenes: the composite of ERS-1 and ALMAZ-1, however, leads to 8 classes even when the cluster distance is trebled (100 grey levels). The object windows selected served also as training sets for a supervised classification of the SAR composite. It could be shown, however, that the two-dimensional histograms of the training sets strongly penetrate each other in the feature space. Therefore, the definition of a discriminance function or separability turned out to be very difficult. The results of the supervised classifica-
Volume 49, number 3, 1994
17
Figure 3. Variance values of ERS-1/ALMAZ-1 selected object windows: I = airport; 2 = lake 1; 3 = lake 2; 4 = forest (M); 5 = forest (D); 6 = suburban area; 7 = innercity;8, 9 = industrialarea. tion with the Maximum-Likelihood procedure did not significantly improve the results obtained by the unsupervised classifications (e.g. MinimumDistance method). The integration of the texturecoded ERS-1 scene in the composite of ERS-1 and ALMAZ-1 increased the separation between areal objects significantly. All the colour composites generated during this work confirmed the statistical evaluation. In addition to the composites of both SAR systems, synergistic data sets between radar and Landsat Thematic Mapper (TM) data were created for comparative purposes. Out of the seven TM bands, three channels in the NIR and MIR were selected and imposed by an IHS transformation. The intensity band was then replaced by either SAR data set. The colour composites ALMAZ-1/ Landsat TM turned out to show more topographic features than the ERS-1/Landsat TM composite (Fig. 4).
olution - - to be more successful in detecting linear structures than ERS-1 data, On the other hand, ERS-1 data generally showed better ability to differentiate areal objects (e.g. separation between densely built-up inner city and loosely built-up suburban areas), whereas ALMAZ-1 data led to better discrimination between different forest stands. The ripple effect of roughened water surfaces (in ERS1) clearly supported the unambiguous separation between land and water. The best results in object recognition were achieved by creating composites of both SAR data systems. In this way the advantages of the multifrequency concept, already verified during extensive airborne SAR campaigns in Europe and elsewhere (Keydel, 1986; Brown et al., 1992) have been confirmed. For improving classification accuracies, the derivation and integration of additional spatial feature levels looks promising. Texture parameters derived from second-order frequency matrices could be valuable tools for that purpose.
4.3. Conclusion
The comparative analysis of the information content of SAR images of an urban area proved ALMAZ-1 data - - due to their higher spatial res-
Acknowledgement The support of the ESA in 1992 is gratefully acknowledged.
18
ISPRS Journal of Photograrnmelty and Remote Sensing
Figure 4. Section of test area. Left: composite of ERS-1 and Landsat-TM. Right: composite of ALMAZ-1 and Landsat-TM.
References ALMAZ-I, 1991. Product Specification Note. Hughes JTX Corporation. Brown, R.J., Manore, M.J. and Poirier, S., 1992. Correlations between X-, C-, and L-band imagery within an agricultural environment. Int. J. Remote Sensing, 13 (9): 16451661. Cappellini, V., Constantinides, A.G. and Emiliani, E, 1978. Digital Filters and their Applications. Academic Press, London, 393 pp. ESA, 1992. ERS-1 User Handbook. Nordwijk.
Keydel, W., 1986. X-SAR extcnds the frequency range of Shuttle imaging radar. Proc. IGARSS' 86 Symp. ESA, Ztirich, pp. 1207-1212. Lichtenegger, J., 1992. ERS-I: landuse mapping and crop monitoring: a first close look lo SAR data. Earth Observ. Q., 3 7 - 3 8 : 1 - 5 Der Senator for Stadtentwicklung und Umweltschutz des [.andes Berlin, 1985/86. Umweltatlas Berlin. Kulturbuchverlag, Berlin. (Received January 25, 1993; revised and accepted May 16, 1993)