Multisource classification of SAR images with the use of ... - CiteSeerX

Multisource classification of SAR images with the use of segmentation, polarimetry, texture

ã

and multitemporal data 1

1

Franck Sery , Danielle Ducrot-Gambart

1

, Armand Lopes , Roger Fjørtoft

3

1,2

,

2

Eliane Cubero-Castan , Philippe Marthon 1

CESBIO (CNES/CNRS/UPS), 18 Avenue Edouard Belin, 31401 Toulouse Cedex 4, France

Tel : (33) 61 55 64 84, Fax : (33) 61 55 85 00 E-mail : [email protected], [email protected], [email protected] 2

ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse Cedex 7, France

E-mail : Fjø[email protected], [email protected] 3

CNES, 18 Avenue Edouard Belin, 31401 Toulouse Cedex 4, France

ABSTRACT distribution

: The

multilook polarimetric maximum

supposes

no

variation

of

the

likelihood

backscattering

of

the

classifier based underlying

on

the

Wishart

scene.

For

clutters

verifying the « product model », we here present the use of a K-distribution and compare this classifier to the one based on the Wishart distribution. A simple way to obtain a full polarimetric filter by filtering a set of adequate powers is also given. We show how filtering and segmentation of the raw data improve the classification results.

KEY

WORDS

:

SAR

images,

multitemporal

and

multisource

classification,

segmentation,

polarimetry, texture, speckle filtering

1. INTRODUCTION Statistical distributions of fully developped speckle in regions of stable underlying reflectivity have been established theoretically for full polarimetric data [1]. These distributions have been used to define maximum likelihood classifiers [2,3]. The optimality of a maximum likelihood classifier is related to the goodness of fit of the real distributions of the Covariance Matrices (CM) of the classes to the supposed distributions. A more realistic case, especially for natural clutters [4], is to consider the underlying reflectivity as a random variable. For a single intensity channel, the multiplicative speckle model (I=RF, where I is the observed intensity, R the true radar reflectivity and F the speckle fading variable) has been verified for a great number of clutters. For the most commonly used scene distributions, such as the Gamma or the Beta distributions of first and second kind for R, theoretical distributions of the intensity have been derived which lead, for the Gamma case, to a K-distribution and, for the Beta cases, to distributions involving Whittaker functions. Under the assumption that the clutters verify the hypothesis of the « product model », Yueh et al [5] and Lopes et al[6] derived similar distributions for CM of full polarimetric data. In this paper we compare the classification accuracies of the Wishart distribution [3] and the K-distribution on a region containing a great variety of clutter types.

ã Copyright

1996 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proc. EurOpto

Satellite Remote Sensing III, Image and Signal Processing, vol. SPIE 2955, Taormina, Italy, September 1996, and is made available as an electronic reprint with permission of SPIE. Single print or electronic copies for personal use only are allowed. Systematic or multiple reproduction, or distribution to multiple locations through an electronic listserver or other electronic means, or duplication of any material in this paper for a fee or for commercial purposes is prohibited. By choosing to view or print this document, you agree to all the provisions of the copyright law protecting it. 1996 SPIE.

When dealing with full polarimetric data, a set of optimum parameters can be extracted from the CM [7]. These parameters represent some aspects of the scattering mechanism of the object

and may be

interesting for classification purposes. A part of this work is devoted to the analysis of the capacity of discrimination of these parameters.

Reducing the speckle variance of the data generally improves classification accuracies. A simple averaging process is optimum only in the case of homogeneous regions, for heterogeneous regions, Lopes et al[6] derived full polarimetric filters based on various estimators of the local texture. In this paper, a decomposition of the CM into powers [8] is used to obtain a full polarimetric filter for which

no particular model

is assumed.

For texture estimations,

the

a

posteriori

mean

(APM)

estimator for gamma distributed texture [9] is presented.

A new approach to the segmentation of raw SAR images was recently proposed by Fjørtoft et al. [10]. This method combines the optimum filter of Shen and Castan [11], the ratio detector [12], the watershed algorithm [9] and a Student’s test for region merging. Segmentation of SAR images generally correspond to a map of homogeneous parts of the image and can be used as an edge detector for filtering. Images of agricultural landscapes are composed of homogeneous fields and the segmentation will here be used for regionwise classification. A multi-temporal set of ERS 1 images is classified in this way in the second part of the paper and comparison are made with per-pixel SPOT data classification.

2. FULL POLARIMETRIC FILTER

Barnes[8] proposed a method to obtain full polarimetric data by using some powers corresponding to particular transmitted and received polarisations. In the case of reciprocal polarimetric data, the total power when the radar transmits a t polarized wave and receives an r polarized one is :

P rt

t

= r. St = (r 2

1

r2

S )æçè S hh hv

ö÷ æç t ö÷ S vv ø è t ø

S hv

2

1

(2.1)

2

where r and t are normalized vectors and S is the scattering matrix. From the particular vectors H,V,F,O,L and R, which are the horizontal, vertical, 45° linear, 135°

( ) =( )

()

(- )

()

linear, left circular and right circular polarized vectors, respectively : H

=

1 0

0

,V

1

,F

=

1 2

1 1

,O

=

1 2

1

1

,L

=

1 2

1 j

,R

=

1 2

(- ) 1

j

,

(2.2)

9 particular powers are extracted and expressed in terms of the elements of the Mueller or the covariance matrix :

P HH

= S hh

= P VF = P FO = P FL = P HF

1 2 1 2 1 4 1 4

2

, P VV

= S vv

2

, P HV

= S hv

2

,

+ P HV ) + Re( S hh S hv ) , P HL = ( P HH + P HV ) + Im( S hh S hv ) , (P VV + P HV ) + Re( S vv S hv ) , P VL = ( P VV + P HV ) - Im( S vv S hv ) , ( P VV + P HH ) Re( S hh S vv ) , ( P HH + 2 P HV + P VV +2 Re( S hh S hv ) + 2 Im( S hh S hv ) *

( P HH

*

1 2

*

*

1 2

*

1 2

*

*

+ 2 Re( S vv S hv ) - 2 Im( S vv S hv ) + 2 Im(S hh S vv )) *

*

*

(2.3)

This system is invertible and the elements of the covariance matrix can be retrieved from these 9 particular powers :

= P HF - ( P HH + P HV ) , Im(S hh S hv ) = P HL - ( P HH + P HV ) , Re( S vv S hv ) = P VF ( P VV + P HV ) , Im( S vv S hv ) = - P VL + ( P VV + P HV ) , Re( S S ) = - 2 P hh vv FO + ( P VV + P HH ) , Im(S hh S vv ) = ( P VV + P HH ) + P HV +2 P FL - P HF - P HL - P VL - P VF Re( S

*

*

1 2

hh S hv ) *

*

1 2

*

1 2

1 2

*

1 2

1 2

(2.4)

In this way Barnes defined a linear relation beetween the elements of the covariance matrix and some powers. This relation is

similar

to

the

one which relies

the

covariance

matrix and

the

Stokes

operator, a new equivalent set of features is thus given to the user.

These powers, as Barnes suggested, could have been measured

directly

and the

hypothesis

of

multiplicative speckle can be made on all these powers. This probably missing property of the elements of the covariance matrix [13] may be the main reason why the results obtained with the multilook LMMSE polarimetric filter [14] have been disappointing.

In the case of filtering, using techniques such as Lee’s multipolarization filter[15], Compensated Texture Optimal Summation (CTOS) [6], or separate processing of each power by scalar LMMSE, Kuan or GAMMA-GAMMA-MAP filters, becomes a full polarimetric filter which preserves the complex degrees of coherence.

For this study we used the CTOS which gives the best compromize beetween speckle smoothing and details preservation. For the estimation of the local texture parameter as for the post-filtering, we used a new scalar estimator presented by Lopes et al [9] : the a posteriori mean (APM) of the texture parameter in the case of Gamma distributed texture and speckle. This estimator is the one which minimizes the Mean Square Error (MSE) and which is unbiased even in case of strong textural variations (the MAP is shown to be biased in this case [9]). This estimator is given by :

R APM

where i 0

=

aLI

4




=

K i0

a - L +1(

K a -L(

, L is the number of looks,

i

0

)

i0 )

1

a

2

< R>

(2.5)

a is the shape parameter of the Gamma distribution of

the texture and is the average value of the radar reflectivity R.

3. SEGMENTATION

Several robust segmentation schemes for SAR images have been proposed over the last few years [9,16,17]. The segmentation method used here is based on edge detection and region growing [10]. First we compute an edge strength map using the Ratio Of

Exponentially Weighted

Averages

(ROEWA) operator, which is a spatially optimum multi-edge detector for images degraded by multiplicative white noise. It can be considered as a modified version of the optimal edge detector proposed by Shen and Castan [10]. The modification consists in replacing the difference of the exponentially weighted averages computed on each side of the central pixel by a normalized ratio.

Touzi et al [11] demonstrated that the traditional difference-based edge detectors, developed for optical imagery, give a higher false alarm rate in zones of high reflectivity than in zones of low reflectivity when applied to SAR images, whereas ratio-based detectors give a constant false alarm rate. It was recently shown by Oliver et al [18] that the ratio measure coincides with the maximum likelihood edge detection measure for SAR images if the ratio of averages is calculated on a sliding window

split

into

two

equal

sized

halves.

This

is

the

case

for

the

ROEWA

operator.

The

exponentially weighted averages can be computed very efficiently using recursive (infinite impulse response) filters [9,10].

Next, a simple and efficient thresholding method, the watershed algorithm [19], is used to obtain closed,

skeleton

boundaries.

The

choice

of

the

detection

threshold

is

delicate.

It

may

be

set

according to the accepted probability of false alarm [12,18]. However, this approach is not well adapted to the watershed algorithm, so we have chosen a different strategy. In order to detect all significant edges, we accept the detection of numerous false edges as well. We deliberately create an oversegmented image by choosing a low threshold.

In the final stage, we reduce the number of false edges by merging regions whose mean values are not significantly different according to a Student's t-test. This criterion seems to eliminate false edges efficiently without merging regions that are visibly different. It should therefore be well suited for applications which need a partition in thematic regions, eg classification.

The

order

in

which

the

regions

are

merged

has

a

great

influence

on

the

final

result.

The

determination of a globally optimal merging order implies extensive sorting and updating of the relations between adjacent regions. We have chosen to use the iterative pairwise mutually best merge criterion [20], which is a locally optimal approach that requires no sorting.

Applying the segmentation procedure on the 9 powers simultaneously is a time-consuming process, but a satisfying result can be obtained by treating only the first principal component or the mean of the powers.

Due to the sensibility of the backscattered intensity to many different parameters, such as soil moisture,

terrain

variations

or

forest

cover

changes,

the

segmentation

is

more

a

map

of

homogeneous regions than a real description of the objects present in the image. The principal interest of the segmentation is a precise detection of edges. It has been used here as an edge detector for the filtering process. A more comprehensive study of speckle filters using segmentation can be found in Fjørtoft et al [21].

4. POLARIMETRIC DISCRIMINATORS

Touzi et al [7] showed that most of the known optimum polarimetric discriminators were highly correlated with the extrema (Min and Max) of the degree of polarization or of the total scattered intensity. In this work, we use some particular values of these two discriminators as the maximum, the minimum, the mean and the standard deviation of the values taken when the polarization of the transmitted wave is varying. We also calculate the same special values of the modulus of the degree of coherence and the phase difference of the received wave :

t ù I 0 é I 0t ù é t t tú ê êQ t ú I 0 cos 2y cos 2 c ú F = ê =ê t t t t tú I sin 2y cos 2 c ú ê U 0 ê tú ú ëV û êë I 0t sin 2 c t û r é + Eh r ù Ev r ú ê éI0 ù ú ê r r êQ r ú - Eh ú Ev ê = Fr= ê ú r êU r ú ê 2 Re( E E ) r ú h ú ëV û ê ê 2 Im( E E h ) r úû ë

Stokes vector transmitted :

Stokes vector received :

2

2

2

2

(4.1)

(4.2)

*

v

*

v

I0 is the total intensity,

Y the rotation angle and c the ellipticity angle.The two vectors are related by

the Mueller matrix M : Fr = MFt

By varying

Y and c of the transmitted vector with

I

(4.3)

t 0

set to one, the particular values are calculated

on the received vector : 2

Qr

degree of polarization =

+U r + V r 2

2

(4.4a)

r

I0

r

total intensity = I 0

(4.4b)

r +V r r r ( I 0 + Q r )( I 0 - Q r ) U

modulus of degree of coherence =

phase difference = arctan(

V

r

2

2

(4.4c)

)

(4.4d)

Ur

5. MAXIMUM LIKELIHOOD CLASSIFIERS

The complex Wishart distribution is generally assumed for covariance matrices of homogeneous regions.

Lee

and

Grunes

particular distribution : let

[3]

describe

w ,...,w 1

NC

the

maximum

likelihood

classifier

corresponding

to

this

be NC classes to be identified and let C1,...,CNC be their mean

covariance matrices. For a L look image, the Wishart pdf of a measured covariance matrix conditional to the class i is given by :

P(

S /w i ) =

L

Lp

p( p-1)

p

2

-1 S L- p e - LTr(C i S )

G ( L). .. G ( L - p + 1) Ci

(5.1)

L

where p=3 in the reciprocal case.

S belongs to class k if :

S)5), the gaussian hypothesis may be done for the distribution of the data, especially when amplitude data or the logarithm of the intensities is considered. In the case of multichannel data, the introduction of calculated correlations beetween channels may balance the possibly poor fit beetween the real distribution and the gaussian one.

However, for non-Wishart distributed and non-K distributed classes, the gaussian distribution is the Maximum Entropy solution when means and covariances are known.

6. RESULTS AND DISCUSSION

6.1 Site of NEZER, Les Landes, France.

The

data

are

from

the

SIRC

campaign

of

1994

over

Southern

France

:

L

and

C

band

full

polarimetric. 10 looks have been estimated by the method of moments on homogeneous regions visually chosen in the image.

Among the 28 classes of the ground truth, 20 separable classes have been kept : 4 differently aged forest plantations, natural forest, cultures (mainly corn), bare soil, road, railroad, landing strip, sand, sand bank, dune, beach, oyster park, fairway, urban area and lake. Some of these are heavily textured, such as oyster park, urban area, sand bank. The ground truths have permitted to collect sufficiently large samples, which have been split into two equal parts, one is used to estimate parameters for the classification procedures, the other serves to calculate classification accuracy represented by a confusion matrix. Tables presented below will show the diagonal elements of the confusion matrices, which represent the percentage of well classified points per classes.

Table 1 presents results of classification obtained on raw data. When applied to amplitudes or logarithm

of

intensities,

the

gaussian

distribution

gives,

on

the

average,

better

classification

accuracies than Wishart or K distributions. Classes with little or no texture, such as forest, are easier to discriminate when using the Wishart distribution. The K distribution should be used for textured classes. The gaussian distribution is less sensitive to variations in texture and gives similar results for all non forest classes. Forest classes are the least separable ones with similar correlations beetween intensities. For these classes full polarimetric information is necessary.

WELL

Int. Gauss. hyp.

Amp. Gauss. hyp.

Log(I) Gauss. hyp.

CM K-dist. hyp.

Lee CM

CLASSIFIED forest 2 years

60.07

62.25

60.07

69.46

70.69

forest 3-4 years

24.14

30.05

34.76

45.72

45.35

forest 6-7 years

54.17

54.24

49.90

52.12

55.30

forest >12 years

52.64

58.49

62.51

69.81

71.36

natural forest

52.73

60.15

65.97

70.29

67.19

corn, cultures

74.86

78.33

83.37

76.26

68.24

bare soil

70.18

77.63

82.55

83.25

80.12

road, motorway

42.45

64.75

74.10

67.63

57.55

landing strip

53.19

73.40

81.91

87.23

82.98

railroad

76.24

78.22

80.20

73.27

83.17

urban area I

62.13

73.69

82.78

58.75

68.36

urban area II

87.16

88.76

89.45

92.20

94.50

sand

81.77

92.31

98.29

87.46

78.06

dune I

97.10

98.55

98.19

98.55

98.91

dune II

74.46

86.96

92.93

92.93

84.78

sand banks

63.19

72.83

80.30

80.48

69.00

oyster parks

31.43

32.08

37.43

40.97

34.08

fairway I

49.06

70.19

80.97

67.44

68.76

fairway II

88.54

88.95

88.34

90.22

92.31

lake, water

94.26

95.69

95.89

98.11

98.50

mean value

64.49

71.88

76.00

75.11

73.46

Table 1 : Classifications performed on raw data (% well classified)

Table 2 shows results of classifications obtained on filtered data. When the correlation beetween channels is strong, the CTOS has a poor filtering effect. In this case, Bruniquel et al [6] suggested to post-process each resulting channel independently with a spatial filter. Some of the nine power channels (refer to section 2) are highly correlated and after CTOS the channels are estimated to have an ENL of 25 looks. Most of the tests are made on post-filtered data (denoted by FCTOS in Table 2), but it should be noted that CTOS-filtered data still fit the Wishart and K distributions with same proportionnality beetween results. The CTOS is thus a simple way to improve classification results of raw data with reasonable loss of spatial resolution.

WELL

CTOS K-dist.

CLASSIFIED

CM

CTOS Lee CM

FCTOS Gauss.

FCTOS Gauss.

FCTOS Gauss.

FCTOS Wish

FCTOS Lee

Int.

Amp.

Log(I)

CM

CM

forest 2 years

75.95

74.83

78.41

79.81

80.26

67.28

83.56

forest 3-4 years

53.48

51.42

43.9

48.68

53.08

21.98

59.51

forest 6-7 years

64.92

64.79

75.55

75.26

75.26

91.21

77.28

forest>12 years

79.51

76.24

76.47

80.24

82.19

84.48

87.75

nat. forest

78.24

71.66

75.43

78.79

79.7

39.61

80.51

corn, cultures

78.58

68.32

80.98

85.28

89

69.64

66.25

bare soil

89.02

80.91

86.73

88.72

90.31

85.24

88.07

road, motorway

61.15

58.99

51.8

66.91

75.54

53.24

62.59

landing strip

81.91

87.23

62.77

86.17

87.23

95.74

87.23

railroad

65.35

85.15

87.13

89.11

86.14

75.25

93.07

urban area I

53.76

69.03

77.07

84.3

90.15

68.51

72.65

urban ar. II

97.48

99.54

97.94

98.17

99.08

97.94

100

sand

86.89

79.49

84.9

95.44

96.87

83.19

83.19

dune I

98.19

99.64

96.01

94.93

93.48

98.55

99.64

dune II

94.57

91.85

92.93

94.57

96.74

88.59

97.83

sand banks

84.07

69.46

79.91

86.42

90.6

59.2

73.97

oyster parks

45.26

35.79

43.38

53.33

60.04

29.9

37.96

fairway I

77.01

73.82

70.74

85.48

89

4.73

77.23

fairway II

89.51

90.33

89

89.51

91.34

88.59

94.2

lake, water

99.93

99.35

98.83

99.22

99.61

94.65

99.93

mean value

77.74

76.40

77.49

83.02

85.28

69.88

81.12

Table 2 : Classification performed on filtered data (% well classified)

The filtering method does not alter the polarimetric properties of the classes. It improves the mean classification results by about 10%. The fit to Wishart distributions yields poor results, probably due to the great sensibility of the Wishart distribution to its L parameter when L tends to p.

The last part of the study of polarimetric data considers the use of polarimetric discriminators to improve classification results. We use discriminators calculated on filtered CM. The main drawback, when using these parameters, is that no particular distribution is available to describe their variations. We assume here that they are gaussian distributed. Table 3 shows classification results of some combinations of these discriminators. The most powerful parameters are the total intensities. In fact, these parameters are higly correlated to the co-pol intensities. If we replace the co-pol intensities by the minimum and maximum values of the total intensities, no significant change in classification results

can

be

observed.

Most

of

these

parameters

contain

similar

information.

A

principal

component analysis applied to all parameters extracts 5 channels containing 99.5% of the total information. The first idea was to use them to perform a first bounding elimination of classes but no real improvement has been observed in this way. These observations should be related strictly to

these data or to SIRC multilook polarimetric data. Improvements have been obtained on airborne data [24] with a filter using an 11 by 11 processing window.

WELL

Deg. of Pol.

Tot. Int. Log

Deg. Coher.

Diff. Phase

Ihv, TI min, TI

CLASSIFIED

Deg pol + Deg coher

max

forest 2 years

62.42

74.61

54.19

26.51

78.58

62.92

forest 3-4 yrs

32.73

16.16

36.39

0.37

50.71

45.44

forest 6-7 yrs

46.73

48.17

49.81

0.03

74.55

48.2

forest >12 yrs

56.13

56.52

42.81

2.21

82.06

59.72

natural forest

29.56

70.41

19.11

5.17

76.04

42.45

corn, cultures

47.64

82.3

52.52

1.16

90.65

56.49

bare soil

77.44

86.33

65.56

51.04

89.12

82.65

road

17.27

64.03

13.67

28.06

79.86

15.83

landing strip

55.32

84.04

41.49

14.89

94.68

51.06

19.8

83.17

4.95

15.84

88.12

13.86

urban area I

50.48

66.22

54.76

8.99

89.3

63.56

urban area II

71.33

87.16

58.49

2.52

92.66

81.19

sand

90.88

87.18

93.73

51.85

95.44

94.3

dune I

36.96

88.41

36.59

32.61

98.19

51.45

dune II

18.48

64.67

26.09

11.96

94.57

16.3

sand banks

39.04

63.69

18.16

34.47

91.43

45.22

oyster parks

10.65

17.54

9.24

0.71

58.98

29.08

fairway I

68.21

74.26

38.28

3.08

89.88

71.4

fairway II

66.14

91.24

55.35

1.58

90.02

74.03

89.5

95.89

86.82

75.73

99.67

91.72

49.33

70.1

42.90

18.44

85.23

54.84

railroad

lake, water

mean value

Table 3 : Classifications performed on polarimetric discriminators (% well classified)

6.2 Agricultural site of Bourges, France.

A multi-temporal set of 6 ERS 1 data is available on this site : 03/09/93, 04/30/93, 06/04/93, 07/09/93, 08/13/93 and 09/17/93. 2 dates of SPOT 3-frequency optical data were also available : 06/09/93 and 08/26/93.

Ground truths were originally undertaken for a project involving airborne data, so only small sized samples could be extracted. For this study a single sample collection has been used to initiate and to verify the methods. For comparison, SPOT data have been superposed the ERS 1 data and samples were collected visually on the rectified SPOT data.

15 classes are studied : forest, wheat, tender wheat, corn, sunflower, barley, colza, pea, clover, grass-land, ray grass, bare soil, road, water and urban area.

We compare the following distributions : Gamma distribution with fixed L parameter [25], Kdistribution, Gaussian distribution on logarithm of intensities and fitted Gamma distributions for filtered intensities.

When treating entire regions defined by a segmentation obtained prior to classification, the class of maximum

occurrence

distribution

is

is

preferred

chosen

in

to

classical

the

all

cases.

For

gaussian

the

gaussian

one.

The

hypothesis,

Gauss-Wishart

the

Gauss-Wishart

distribution

is

the

distribution of the sample mean vector and sample covariance matrix of an n-variate gaussian vector X. For a region of size N, the conditional probability of

m

j

and Cj of class j is :

P(

m j ,C j / m, S ) = (2p )

- n/2 NC

-1

1

- N ( m - m ) C -1( m - m )

1/ 2

j

e

x c( n, N )

ì

with

c( n, N ) = 1 / íp

n( n

-1 )/ 4

î

ÕG N - i n

i

[(

=1

j

2

1

N

2

ü

S

j

(

j

N -1)/ 2

1

- Tr ( NS C -1) -1 ( N - n - 2 )/ 2 2 j

C

e

j

) / 2]ý

(6.2.1)

þ

Table 4 shows results of classifications by regions. For classes with few test points, a misclassified label may have a great influence on the overall accuracy. This explains the poor percentage obtained for

grass-land. Corn

and

bare soil

have

similar

mean

reflectivity

at

several

dates

and

a

great

confusion beetween these two classes is observed. These dates do not correspond to the full growth of the corn. Apart from these classes, the method gives promising results. 6 dates of ERS 1 data are necessary to obtain a result similar to the one obtained with 2 dates of SPOT data (Table 5).

well classified

Gamma raw data

K dist. raw data

Gauss-Wish raw

Gauss-Wish filtered

Fitted

Gamma

filtered forest

85.40

86.94

85.93

86.84

82.73

wheat

50.39

70.34

51.54

57.81

66.53

tender wheat

61.27

55.06

68.98

59.30

37.32

corn

22.04

17.88

30.93

44.79

13.16

sunflower

42.14

52.96

63.56

79.83

67.31

barley

59.24

71.74

55.43

56.52

83.15

colza

84.63

87.49

87.06

85.55

86.08

peas

81.98

82.19

79.49

89.39

86.68

clover

65.55

65.55

70.60

70.51

57.44

grass-land

25.53

26.60

26.60

47.87

47.87

ray_grass

48.15

31.64

77.70

88.43

28.85

bare soil

35.41

36.98

49.92

60.83

37.10

0.00

79.56

79.56

79.56

80.29

water

86.71

89.18

76.42

75.48

90.78

city

70.85

74.07

83.89

89.51

77.95

mean

54.62

61.88

65.84

71.48

62.88

road

Table 4 : Bourges, classification by regions (% well classified)

well classified

Fitted Gamma 6/7/8/9

Gauss-Wishart 6/7/8/9

forest

SPOT 6/8 94.21

64.56

75.48

wheat

77.91

69.95

57.11

tender wheat

79.55

70.94

61.8

corn

88.71

54.97

32.37

sunflower

88.28

80.93

80.24

barley

52.72

56.52

55.43

colza

76.08

83.53

85.76

peas

67.31

85.4

68.87

clover

86.83

48.78

48.87

grass-land

52.13

41.49

48.94

ray-grass

88.29

60.07

76.03

bare soil

86.18

50.92

38.67

road

54.01

78.83

79.56

water

78.42

86.84

74.01

city

93.91

78.96

85.21

mean

77.64

67.51

64.56

Table 5 : Comparison with SPOT (% well classified)

7. CONCLUSION

In this paper we present a complete scheme for the analysis of polarimetric data. A full polarimetric filter is proposed and some new estimators and distributions are tested. Comparisons were made to validate the product model : the K-distribution has been shown to be more realistic than the Wishart distribution for most of the classes.

The map of edges given by the segmentation has greatly facilitated all the treatments. With this segmentation a classification of agricultural area with ERS 1 can be done in a fast and efficient way.

8. ACKNOWLEDGEMENTS

Special thanks to Thuy Le Toan and the Jet Propulsion Laboratory for providing the SIRC data. The work on Bourges was part of the contract 833/CNES/94/1022/00 with the Centre National d’Etudes Spatiales (Toulouse, France).

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