Multisource data fusion- fusing optical and sar data
for irrigated rice areas identification
Jirathana Worawattanamateekul
Canisius X.J.F.
Lal Samarakoon
Asian Center for Research on Remote Sensing
Asian Institute of Technology
P.O.Box 4 Klong Luang, Pathumthani 12120
Tel: (66)-2-524-6148 Fax: (66)-2-524-6147
E-mail: jirathan@ait.ac.th
THAILAND
Keywords: Data Fusion, Principal Component Analysis, Thematic Combination
Abstract:
Food security has become a global key issue, and this is a major concern
particularly to Asian region due to rapid expansion of Asian population. In many countries of
the region, accurate evaluation of food production and estimation is not possible due to lack of
information. Further, insufficient or obsolete information hampers timely solutions when there is
a decrease in production, and introducing appropriate solutions for increase productivity. One of
the basic information that is not available in most of the countries in Asian region is the
cultivated area that could keep the planers well informed of the future harvest, and prepared for
food crisis in advance. This paper examined the potential of satellite remote sensing in
estimating irrigated paddy cultivated areas in a test site in Indonesia. Due to frequent cloud
cover in this area solely rely on optical sensor data is a limiting factor of using satellite data for
mapping. Attempt was made to integrate SAR data acquired during growing period with optical
data to overcome this limitation. Data from JERS-1, optical and SAR data was used for the
study. Applying various fusion methods, it was found that combination of vegetation index,
average intensities of SAR, and principal component of optical data gives the optimal solution
for the test area. Results proved data fusion from different sources acquired in various stages
irrespective to their source could satisfactorily be used in estimating irrigated paddy area under
cultivation.
1. Introduction
Data fusion means a very wide domain and it is rather difficult to provide a precise definition.
Several definitions of data fusion have been proposed. Pohl and Van Genderen (Wald, 1999)
defined “ image fusion is the combination of two or more different images to form a new image
by using a certain algorithm” which is restricted to image. Hall and Llinas (Wald, 1999) defined
“data fusion techniques combine data from multiple sensors, and related information from
associated databases, to achieve improved accuracy and more specific inferences that could be
achieved by the use of single sensor alone”. This definition focused on information quality and
fusion methods. According to these definitions, it could imply that purposes of data fusion
should be the information obtained that hopefully should at least improve image visualization
and interpretation.
There are several fusion approaches, generally, fusion can be divided into three main categories
based on the stage at which the fusion is performed namely: pixel based, feature based and
decision based. In pixel based fusion, the data are merged on a pixel-by-pixel basis. Feature
based approach always merge the different data sources at the intermediate level. Each image
from different sources is segmented and the segmented images are fused together. Decision
based fusion, the outputs of each of the single source interpretation are combined to create a new
interpretation.
Although there are many data sources for the purpose of fusion, this study was mainly dedicated
to only remote sensing data fusion and their visualization with the following possibilities;
multitemporal and multisensor data fusion. Several remote sensing data have been acquired and
some possible fusion techniques were applied to these data to generate image fusion results.
Results of fused data were demonstrated and interpreted in terms of its usefulness in irrigated
rice field identification.
2.Test Area and Data Used
Semarang is the selected test area and located in Central part of Java Island, Indonesia. This area
is predominated by paddy rice. Both optical and SAR data were used in this study. Figure 1
shows false color composite of JERS-OPS data of test area, table 1 present satellite data
descriptions used in the study.
Figure 1: False Color Com posite Image of the Test Area
Table 1: Descriptions of Remote Sensing Data Used
| Satellite | Sensor | Date |
| JERS-OPS | Visible-NIR | 24/8/96 |
| JERS | SAR | 29/09/96 |
| JERS | SAR | 12/11/96 |
| JERS | SAR | 24/03/97 |
Methodology
All remotely sensed data both optical and SAR data required systematic corrections. The data
distributor normally provides this step. Speckle reduction by applying speckle specific filter is
the next process applied to SAR data to reduce the data noise while retaining the information.
Subsequently, 16 bit SAR data were converted to 8 bit data to be able to compare with 8 bit optical data. In the optical side, the data were necessary to go through atmospheric correction
step. Then, both optical and radar data were coregistered in order to be fused together. Finally,
the visualization step presents results of fusing data. Figure 2 shows general steps in fusion
process.
Figure 2: Data Fusion Process
Figure 2 shows general steps in data fusion process. However, it is necessary keeping in mind
that the previous steps in the flow are crucial for the succeeding steps and therefore to the
overall accuracy of the image map. Selection of data for fusion is very important as fusing
inappropriate data could deteriorate information content.
All optical and SAR data were pre-processed and coregistered into the same coordinate system.
At the fusion step, we applied several fusion techniques and compared the fused results. Those
techniques are overlay, principal component analysis (PCA) and thematic combinations.
