Land cover classification and interpretation of NASA / JPL AIRSAR data based on scattering mechanisms and statistical distribution
Ken Yoong LEE
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Block SOC1, Level 2
Lower Kent Ridge Road
Singapore 119260
Tel: (65) 6874 8029
Fax: (65) 6775 7717
E-mail: crsleeky@nus.edu.sg
Soo Chin LIEW
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Block SOC1, Level 2
Lower Kent Ridge Road
Singapore 119260
Tel: (65) 6874 8029
Fax: (65) 6775 7717
E-mail: crslsc@nus.edu.sg
Leong Keong KWOH
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Block SOC1, Level 2
Lower Kent Ridge Road
Singapore 119260
Tel: (65) 6874 8029
Fax: (65) 6775 7717
E-mail: crsklk@nus.edu.sg
Abstract
In previous work (Lee et. al, 2001), we have shown the land cover classification of NASA / JPL single-frequency
C- and L-band fully polarimetric POLSAR data over the northern part of Peninsular Malaysia,
based on both physical and statistical properties. Further extension on various frequency and
polarization combinations was investigated and reported in this paper. In unsupervised classification, the
scattering mechanism of each pixel in Lee polarimetric filtered images was analyzed using Cloude and
Pottier's target decomposition theorem and Van Zyl approach, respectively. The supervised complex
Wishart classifier was used to classify the following inputs: multifrequency fully polarimetric SAR, single-frequency
fully polarimetric SAR, and dual-polarization complex SAR. For single-frequency and single-polarization
SAR intensity data, the supervised Maximum Likelihood classification was performed based
on Gamma distribution. The Kappa statistics computed for classification using single-frequency fully
polarimetric C- and L-band data were 0.69 and 0.73, respectively. An improvement to 0.79 was
achieved by combining C- and L-bands fully polarimetric data in the classification. Only a limited number
of land cover classes could be discriminated from C- and L -band intensity data.
Introduction
Spaceborne remote sensing has long been an appropriate and effective data source for land cover
mapping due to the wide coverage and repetitive observations (Haack and English, 1996). In general,
there exist two major types of remotely sensed data: optical and synthetic aperture radar (SAR). Both
optical and SAR data, however, have certain problems in their applications in mapping the tropical
regions. The occurrence of extensive clouds is the main problem of the optical remote sensing data. As
clouds interfere with the reception of optical sensors, its presence consequently causes the loss of land
cover information in the captured data. At present, only single-frequency and single-polarization SAR is
currently available on space-platforms such as the ERS 1/2 and RADARSAT-1. Land cover
classification using single-frequency single-polarization SAR data is difficult due to the poor separability.of different land cover features. Nevertheless, with the advent of multifrequency and multipolarization
SAR system, it is now possible to capture the full-polarimetric signatures of the earth surface, enabling a
more accurate classification of the various land cover types (Boerner et. al, 1998). The ASAR instrument
on-board the recently launched ENVISAT satellite has the capability of performing alternating
polarization SAR. Other upcoming satellites that promise full-polarimetric SAR imaging capabilities
include RADARSAT-2 and ALOS. The POLSAR data acquired during the NASA / JPL AIRSAR missions
provide an opportunity to investigate the capability of multifrequency and multipolarization SAR in land
cover classification. An attempt is made, in this study, to investigate and optimize the use of
multifrequency and multipolarization SAR data for land cover classification over tropical region. The
classification was carried out based upon the physical (i.e. scattering mechanism) and statistical
properties in the POLSAR data. Focus is placed on the C- and L -band POLSAR data.
This paper is organized as follows: Sections 2 and 3 present the test area selected and data acquired for
this study. In Section 4, the speckle suppression and classification of POLSAR data are discussed.
Section 5 analyzes the results obtained. Concluding remarks are given in Section 6.
Study Area
The test site identified for this study is an agricultural inland region covering an area of approximately
100 km
2
in the northwest of Peninsular Malaysia near Jitra (Figure 1a). It extends from 6° 11’ to 6° 17’ N
latitude and 100° 21’ to 100° 26’ E longitude. The topography is characterized by flat and undulating
terrain, with an elevation of approximately 2.5m. The large portions of the site are the irrigated lands,
predominately cultivated with rice paddy. Other land cover features, such as rubber plantations,
grassland, highway, runway, built-up area, and river, can also be found in the study area. During the
1996 AIRSAR PACRIM Deployment, the site was selected by the Malaysian Centre for Remote Sensing
(MACRES) for land use and rice crop study (Lou et. al, 1997).
Figure 1: Location map of Jitra, Kedah (a) and the corresponding Lee polarimetric filtered (5x5)
POLSAR data (b) as well as the SPOT-1 panchromatic data (c)
