Land cover classification and interpretation of NASA / JPL AIRSAR data based on scattering mechanisms and statistical distribution
Data Acquisition
The data used in this study was provided by NASA / JPL. The POLSAR data, with C- (5.7 cm), L- (25
cm) and P- (68 cm) bands in full polarization (HH, HV, and VV), was acquired using the AIRSAR
instrument on-board a DC-8 aircraft during the PACRIM-1 mission on 3
rd
December 1996. The aircraft
flew at nearly 9 km during data collection. The incidence angles were 24þ for near range and 60þ for far
range corresponding to the near and far ranges of 10 and 20 km, respectively. The 18-looks POLSAR
data supplied by NASA / JPL was projected into ground range with a ground pixel spacing of 10 meters.
Only C- and L-bands (both in Compressed Stokes Matrix format) were examined in this study. A cloud-free
SPOT-1 scene of the same area was also acquired and processed by CRISP (Figure 1c). The 10
meters resolution SPOT panchromatic image was acquired on 18
th
January 1997 and processed to Level
2A. It was used together with the existing topographical and land use maps for locating the test sites.
Methods and Implementation
1. Speckle Suppression Using Polarimetric Filters
The POLSAR images (in complex covariance matrix form) were filtered using two polarimetric filters,
namely Lee (Lee et. al, 1999) and Mean (also known as “boxcar”). Window sizes of 3x3, 5x5, 7x7, were
tested and analyzed. The performance of both polarimetric filters was assessed in terms of speckle
suppression, edge enhancement, and preservation of polarization signatures (Lee et. al, 2001b).
Comparing both Lee and Mean filters, the former was found to be more superior to the latter. Hence, the
Lee filtered images of both C- and L-bands using 5x5 window were selected and employed in the
subsequent classification process.
2. Unsupervised Classification of Scattering Mechanisms
There are two different approaches of unsupervised classification carried out in this study. First, the van
Zyl approach was used to classify the Lee filtered image pixels into three categories: (1) odd number of
reflections, (2) even number of reflections, and (3) diffuse scattering. To define the scattering behavior of
each pixel, the classification involved the use of the elements of Muller matrix. Detailed discussions on
the Muller matrix appear in van Zyl (1989). Each scattering category obtained was then further
separated into two groups: |HH|
2>|VV|
2and |HH|
2<|VV|
2. Figures 2(a) and 2(b) show the classification
outcomes using C- and L -bands, respectively.
Second, the Cloude and Pottier’s target decomposition theorem was studied and employed to group all
pixels into nine different zones (or nine classes) accordingly to the partitioning of the entropy (H)-alpha
(x) plane. The decomposition is based on the eigenvalue analysis of the complex coherency matrix T,
which is based on Pauli matrix representation. The physical scattering characteristics of each zone are
explained in detail in Cloude and Pottier (1997). Figures 3(a) and 3(b) give the C- and L-band
classification outputs resulting from the target decomposition in H-þ plane, respectively.
Figure 2: Unsupervised classification results based on van Zyl approach
Figure 3: Unsupervised classification results based on target decomposition in H- þ þ plane.4.3 Supervised Classification Based on Statistical Distribution
In supervised classification, the complex Wishart classifier (Lee et. al, 2001a) was used to classify the
following inputs: multifrequency fully polarimetric SAR, single-frequency fully polarimetric SAR, and dual-polarization
complex SAR. The complex covariance matrix Z (a Hermitian matrix) of candidate pixel P
was used to compute its distance to target classm. The distance measure is defined by
where C is the mean covariance matrix for target class m. The pixel P is assigned to target class with
the minimum distance measured. J refers to the total number of bands where J = 2 for the classification
of multifrequency fully polarimetric SAR because of the combination of C- and L-bands, and J = 1 for the
single-frequency fully polarimetric SAR and dual-polarization complex SAR in this study. The order of
the covariance matrix is 3× 3 for both multifrequency and single-frequency fully polarimetric SAR, whilst it
is 2× 2 in the case of dual-polarization complex SAR.
For the single-frequency and single-polarization SAR intensity data, the supervised Maximum Likelihood
classification was performed based on Gamma distribution (Nezry et. al, 1996).
where R
k represents the mean radar reflectivity and
sk = {R
k/
sk}
2
where
sk is the standard
deviation of R in class k. A pixel P of radar reflectivity R
p is attributed to class k (among the N separable
target classes) which maximizes the N discriminating functions.
In this study, the training samples were generated from the unsupervised outputs of the target
decomposition. Figure 4 presents the supervised classification results of C- and L-band images.
(a) combined C and L (b) L-band (c) L-band HV and VV (d) L-bandHH
(e) C-band (f) C-band HH and HV (g) C-band HH (h) Color code
Figure 4: Supervised classification results of multifrequency fully polarimetric (a), single-frequency fully polarimetric (b, e), dual polarization complex (c, f), and single-frequency
intensity data (d, g)
