A user friendly statistical system for polarimetric SAR image classification

A., Sidnei}@Dpi.Inpe.Br), C.

Description

Resumen
En este artículo se presenta un sistema para la clasificación de imágenes SAR polarimétricas. Este sistema utiliza información contextual a través de un modelo Markoviano para las clases, además de modelos estadísticos para los datos. El sistema fue desarrollado pensando en el usuario y, por lo tanto, está íntegramente basado en interfaces gráficas. Toda vez que el usuario trata de activar una operación inválida, el sistema le informa la secuencia correcta de pasos. La funcionalidad del sistema se verifica clasificando áreas de cultivo, en una ima-gen SIR-C/X-SAR.
Abstract
This article presents a system for polarimetric SAR image classification. This system uses contextual information through a Markovian model for the classes, besides a statistical model for the data. It is developed with the user in mind and, therefore, it is solely based on graphic user interfaces. The user is prompted with the correct sequence of steps when-ever an invalid option is invoked. The functionality of the system is checked classifying a SIR-C/X-SAR image, where mainly crops are observed.

Sujets

• Clasificación
• Contexto
• Estadística
• Radar de apertura sintética
• Classification
• ConTEXT
• Synthetic aperture radar
• Statistics

Informations

• Publié par: erevistas
• Publié le: 01 janvier 1998
• Nombre de lectures: 12
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Revista de Teledetección. 1998
A user friendly statistical system for polarimetric
SAR image classification
A. H. Correia*, c. da Costa Freitas*, A. c. Frery** and S. J. S. Sant' Anna*
*Instituto Nacional de Pesquisas Espaciais, Divisao de Processamento de lmagens, CP 525, 12201-970 Sao José dos
Campos, SP - Brazil ({correia;corina;sidnei}@dpi.inpe.br)
**Universidade Federal de Pemambuco, Departamento de Informática, CP 7851,50732-970 Recife, PE - Brazil
([e-mail])



RESUMEN ABSTRACT
En este artículo se presenta un sistema para la This article presents a system for polarimetric SAR
clasificación de imágenes SAR polarimétricas. Este image classification. This system uses contextual
sistema utiliza información contextual a través de information through a Markovian model for the
un modelo Markoviano para las clases, además de classes, besides a statistical model for the data. It is
modelos estadísticos para los datos. El sistema fue developed with the user in mind and, therefore, it is
desarrollado pensando en el usuario y, por lo tanto, solely based on graphic user interfaces. The user is
está íntegramente basado en interfaces gráficas. prompted with the correct sequence of steps when-
Toda vez que el usuario trata de activar una opera- ever an invalid option is invoked. The functionality
ción inválida, el sistema le informa la secuencia of the system is checked classifying a SIR-C/X-
correcta de pasos. La funcionalidad del sistema se SAR image, where mainly crops are observed.
verifica clasificando áreas de cultivo, en una ima-
gen SIR-C/X-SAR.

PALABRAS CLAVE: Clasificación, Contexto, KEY WORDS: Classification, Context, Synthetic
Estadística, Radar de apertura sintética. aperture radar, Statistics.



the electromagnetic signal in both horizontal and INTRODUCCION
vertical polarisation and, thus, they mar carry a
The intensification of remote sensing studies in larger amount of information than that available
the field of Synthetic Aperture Radar (SAR) imag- from a single component. Though there is cur-
ing sensors is leading towards a better understand- rently no sensor operating in different bands and
ing of the scattering mechanisms of terrestrial polarisations, studies in this area are useful.
targets in the microwaves spectrum. Besides this, it Several works are devoted to the statistical char-
has led to more dependable applications of SAR acterisation of single-look polarimetric SAR data.
imagery and products to geology, cartography, and The reader is referred to DeGrandi et al. (1992),
other fields of knowledge. Kong, (1988), Lim et al. (1989), Quegan and Rho-
One of the most useful products of digital im- des (1995), Yueh et al. (1989), to name a few.
ages is the result of automatic or semiautomatic The potential of multilook polarimetric data,
data classification. This product is becoming more where each value is the mean over several observa-
and more precise since the Gaussian hypothesis tions, is notorious as presented in Lee and GcuDes
was weakened, and since better suited distributions (1994) and in Lee et al. (1995), for instance. The
for SAR data were incorporated into the process statistical properties of this kind of data have not
(Nezry et al., 1996; Frery et al., 1997a). been fully exploited yet They have the advantage
In Vieira (1996) this improvement becomes evi- of exhibiting a speckle noise reduction as well as
dent: it is shown that for monospectral SAR data, data reduction. The disadvantage is the resolution
the simultaneous use of proper distribution for loss.
each class, along with contextual information, Given the potentiality of polarimetric data for
leads to better classifications than those obtained image classification, there is a need for systems
either by Gaussian fitting and/or by pointwise that use all the information of polarimetric data, in
classification. On the other hand, the use of mono- a manner that the user can handle it easily without
spectral SAR data has its limitations. knowing too much about the complexity of the
The number of studies and applications involv- underlying theory. The authors of this paper have
ing polarimetric SAR data is increasing steadily. no knowledge of such a system implemented in
These data are formed by sending and receiving commercial software.
Nº 10 – Diciembre 1998 1 de 13 A. H. Correia, c. da Costa Freitas, A. c. Frery and S. J. S. Sant' Anna
The objective this paper is to present a system STATISTICAL PROPERTIES OF
for multilook polarimetric SAR image classifica- POLARIMETRIC SAR DATA
tion which was developed to assess the potential of
Data obtained with coherent illumination, as is this kind of data. The system is strongly based on
the case of SAR data, are corrupted by a signal-the statistical properties of the data, and it uses a
dependent noise called speckle. A usual model for Maximum Likelihood (ML) classification as the
the signal and this noise is the Multiplicative initial configuration for a contextual Markovian
Model. It states that, under certain conditions (Tur classification technique: the Iterated Conditional
et al., 1982) the observed value in every pixel is Modes (ICM for short), presented in Vieira (1996).
the outcome of the random variable Z = XY, where The system present in here allows the analysis of
X is the random variable that models the backscat-intensity, phase difference, ratio of intensities and
ter and Y is the one that models the speckle noise, intensity-phase data. These data formats are de-
and fuese last two variables are independent. rived from multilook polarimetric SAR imagery,
Statistical models for multilook polarimetric data and their distributional properties are here recalled.
are derived from the covariance matrix, which The system is based on graphic user interfaces, and
exhibit a complex Wishart distribution (Lee and was developed as an extension of the ENVI (Envi-
Grunes, 1992; Du and Lee, 1996). ronment for Visualizing Images) image processing
Ullaby and Elachi (1990) show that, for satellites system (ENVI, 1996).
that transmit and receive through the same antenna
(which is the usual case), it is possible to suppose POLARIMETRIC SAR SYSTEMS
that S = S Therefore, the matrix presented in HV YH
Conventional SAR systems operate in a single equation (1) can be reduced, without loss of infor-
frequency, with a single antenna of fixed polarisa- mation to
tion for both the transmitted and received signals.
Usually only the intensity or the amplitude data is  S 1 supplied to the user and, as a consequence, any (3)  Z= S2information carried in the phase of the complex 
S electromagnetic signal is lost. 3 
When polarimetric SAR sensors are used, the
full complex signal is recorded and, thus, the re-
where S , 1 ≤ i ≤ 3 denotes S , S and S ini HH HV VVturn in all the configurations (HH, H~ VH and VV)
any convenient order. are fully recorded (intensities or amplitudes and
When the number of elementary scatterers (de-
relative phases). In order to accomplish this for
noted N in equation (2)) is very large, it can be every resolution cell the complex scattering ma-
assumed that the vector Z in equation (3) obeys a
trix, denoted as multivariate complex Gaussian distribution

(Goodman, 1963). This is true if the backscatter X
 S S VV VH  (1) is constant, independently of the imaged area,
S= S S since the speckle Y is assumed to obey a  HV HH
  mu1tivariate complex Gaussian law.
In this work mu1tilook data are considered and,
is measured. Subscripts p, q ∈ {H, V} denote the in order to derive their distributional properties,
transmission and reception components of the vector Z in equation (3) will be, thus, considered
signal, respectively, and elements S are called pq the k-th single-look observation and denoted as
complex scattering amplitude. Sarabandi (1992) Z(k). A fixed number, n, of independent outcomes
shows that of Z are averaged to form the n-1ooks covariance
matrix, given by (Lee et al., 1995)
N
n iφ iφpq n pq (2) S =|S |e = |s | epq pq ∑ pq n
n=1 1(n) * TZ = Z(k) Z (k)∑ n k=1 (4)
where N is the number of scatterers of each reso-
n Tlution element, each having amplitude | s | and where Z*(k) denotes the transposed conjugate pq
of Z(k).
nphase φ . pq
The advantage of working with the covariance Other ways of representing polarimetric data are
(n)matrix, defined as A = nZ , is that it exhibits a the Stokes matrix, the modified Stokes matrix, the
multivariate complex Wishart distribution covariance matrix and the Mueller matrix (Ulaby
(Srivastava, 1963). Its density is given by and Elachi, 1990).

2 de 13 Nº 10 – Diciembre 1998 A user friendly statistical system for polarimetric SAR image classification
qn (n−q) −1 where Z is a normalising constant, 1 is the in-n |z| exp[−nTr(C z)] β A (5) p (z) =(n)Z n dicatar function of the set A, and (s,t) denotes that K(n,q)|C|
co-ordinates s and t are neighbours, then it is said
that