Large area change detection by differencing radiometrically-normalized images
Itthi Trisirisatayawong
Lecturer, Department of Survey Engineering
Chulalongkorn University
Tel: (66)-2-218-6659
Fax: (66)-2-218-6653
E-mail: itthi@chula.ac.th
Thailand
Wallapa Samchimchom
Survey Engineer, Department of Lands
Tel: (66)-2-984-0959
Fax: (66)-2-503 3430
E-mail: wallapa_s@yahoo.com
Thailand
Abstract
Multi-temporal satellite imageries can be used for change detection. Though simple and intuitive, image
differencing technique which is simply done by subtracting pixel brightness values of one image from
another can produce false alarms which are areas that changes are indicated while in fact no changes
occur. Two major factors causing false alarms are different sun angle and atmospheric conditions and
effects from both of which must be eliminated by some pre-differencing process. This paper investigates
band-by-band linear regression method to normalize brightness values of a full-scene Landsat TM
imagery covering eastern part of Thailand with another co-register close-date 12 year apart Landsat TM
scene. The image pair are then differenced and classified by different threshold values to produce
change maps which are further checked against ground-observation data for accuracy assessment. As a
result, suitable threshold value are determined for large area change detection which can be applied in
activities such as environmental monitoring, coastal resource monitoring or urban expansion monitoring.
Introduction
Change detection is one of major applications of satellite imageries. Basically, areas of change can be
identified by taking the difference of pixel value from a pair of co-registered images acquired at different
times. In terms of image processing, this simply is the subtraction of one image from another. Because
brightness values in each band of a multispectral image have an association with different type of land
covers, the change in brightness values over time signify that change in land cover occurs. But in some
applications of change detection, the values used in subtraction may be those obtained after some kind
of transformation has been performed. For example, the tasseled-cap change in greenness was used to
detect crop-type changes and vegetation to nonvegetation changes or vice versa (Fung, 1990).
Ideally, the image pair used in change detection should be taken on the same date of different years, the
so-called anniversary date. Due to many factors, this may be difficult to achieve and using images of
slightly different date are usually the case. Non-surface factors such as atmospheric effects and
illumination difference caused by different sun angle can influence pixel’s brightness value so accurate
normalization to minimize the effects from non-surface factors is therefore essential.
Atmospheric correction schemes which may use the in-situ measurements or pure image-based
techniques such as those described in Chavez Jr (1996) and Hu et al (2000) can be used to correct the
effects caused by atmospheric factors in each image before taking the difference. However, the most
common technique employed in normalization is linear regression using y-intercept and slope
parameters (Jensen et al, 1995). The studies on change detection based on differencing radiometrically-normalized
images have been performed usually on areas relatively small comparing to the area size
covered by a scene of satellite imagery. For example the study site of Fung (1990) was about 280 sq.km
whereas in Heo and Fitzhugh (2000) the study area was about 30 km x 30 km or 900 sq.km..Many studies such as forest monitoring, environmental monitoring or coastal resource monitoring have to
perform on a large area to see pattern of changes and it is not clear whether linear regression model is
suitable in this case. This is because variations of the effects from non-surface factors do not
necessarily follow linear model, and the larger the area the more likely deviation from linear model should
be the case. Subdivision of large study area into smaller portions, each of which a linear regression is
applied, seems to be a solution but this approach may be impractical if many subdivisions are required.
Other factors such as time and budget may constrain the use of this method. Another possible approach
is to apply a linear regression to the whole study area, provided that the non-surface factors especially
atmospheric conditions in the region are known not to change abruptly. This is the approach undertaken
by this study.
Desctiption of study area and Landsat images
The study area is the provinces of Rayong and Chantaburi. The economy of these two eastern provinces
depend on agriculture and fisheries. Both provinces are regarded as the country’s orchard, producing top
quality of fruits such as durian and mangoesteen. The pattern of land cover is dominated by different
type of orchards mixed with rubber plantation, cassava farms, urban areas and many small communities
areas.
Because of its proximity to Bangkok, Rayong has been developed as an industrial base. A deep-sea port
and two industrial estates have caused huge impact on the province. Chantaburi is farther away from
Bangkok and less industrialized than Rayong. In recent years, however, the development of tourism and
shrimp farming along the province’s coast line have also started making impact on the environment and
pattern of landuse. The total area of Rayong and Chantaburi is about 9,890 sq.km.
The study area is part of a much larger area (covering upper part of Gulf of Thailand) being mapped by
the coastal resource inventory and monitoring project, a long term project sponsored by the Department
of Environmental Quality Promotion. The nature of the project demands up-to-date and low-priced data
and remotely-sensed images fit well to this demand.
Two Landsat 5 TM images of the same path-row (128-51) were used as primary data source for this
study. The images were taken on 3 November 2000 and 20 December 1988. The images were rectified,
radiometrically normalized, differenced and tested as detailed in subsequent sections.
