Keywords: Change detection, Yellow River delta, Remote sensing information model.
AbstractInformation about change is necessary for updating land cover maps and the
management of natural resources. Many researches have been undertaken to develop methods of
obtaining change information. Based on the summarization of the methods on change
information extracted from remotely sensed data, the paper promotes the method of change
detection based remote sensing information model. This method is applied to detect the coastal
line change of Yellow River Delta (YRD). It lays the foundation for research on the change
relation of natural and human activity impact each other, and finally aids to study the regional
geographic feature through more than 10 years remote sensing images in YRD.
1. Introduction
Information about change is necessary for updating land cover maps and the management of
natural resources. The information may be obtained by visiting sites on the ground and/ or
extracting it from remotely sensed data. For many of the physical and cultural features on the
landscape there are optimal time periods during which these features may best be observed.
Remotely sensed data acquired at the fixed time interval becomes an important factor. Many
researches have been undertaken to develop methods of obtaining change information. Change
detected from different temporal images usually reflect natural and human activity impact each
other and then can be used to study how to form the regional geographic feature.
As we know, the Yellow River Delta is a delta grows fastest in the world. Because of its new
land forming and unstable environment, its development is far more backward than other
famous large river delta. However, the Yellow River Delta has a good geographic location, rich
natural resources and tremendous developing potentiality. Its development is of great
importance to the development of North China.
This paper first summarizes the methods on change information extracted from remotely
sensed data. Then based on the different object models the method of change detection is mainly
discussed. Finally through more than 10 years change result of Yellow River Delta, we analyze
the coastal line change.
2 Change Detection Methods Based Remote Sensing Data
2.1 General Methods
Temporal feature is important and special in all the system characteristics. Because spatial and
spectral information can be seen from images, temporal feature is relatively abstract, it is
difficult to reflect directly. Its feature only can be seen by the change of spatial and spectral
feature. Some commonly used change detection algorithms are summarized and analyzed as the
following table.
Table 1 Main algorithms of change detection
| Algorithm and example |
Method procedure |
Problem |
| Image Transformation (e.g.
PCA).Fung et al., 1987,
1988.(Eastman et al., 1993,
Bauer et al.,1994) |
Composition of different
temporal data, then classified
or using PCA to transform all
together |
No classified change
information |
| Image Arithmetic Change
Detection.Price et al., 1992. |
Arithmetic operation among
different bands, e.g.
Dijk=Bvijk(1)-Bvijk(2)+C |
No classified change
information |
| Post-Classification Comparison
(Rutchey et al., 1994) |
Classification of remote
sensing images, then
comparison pixel by pixel |
Relay on classification
accuracy and multiple
classification |
| Data sources aided change
detection(Lunetta et al., 1991. |
Map data or GIS aid to
analyze |
Relay on quality of aided
information |
| Spectral vector change
(Michalek et al., 1993,
Johnson et al., 1998) |
Comparison with spectral
vector of different temporal
CMpixel= [Bvijk(2)-Bvijk(1)]2 |
Suitable for region of spectral
change greatly |
2.2 Based on Remote Sensing Information Model Change Detection
Remote sensing information is a complicated information. It is the comprehensive behavior
from a certain environment. The behaviors of images varied largely with different ground
features due to their different radiation and scattering characters to visual light, infrared and
microwave. As a result, we can not build their remote sensing information models for ground
feature under a unified mode. Three levels of model are conducted to describe the ground
feature as follows:
- Spectrum vector based remote sensing information model for ground feature:
Some features, such as water body, vegetation, scene of forest fire, possess distinctive spectrum
characteristic of multiple bands. Remote sensing information is often corresponding to ground
object. This model is the starting point for a study. It is called primitive class model.
- Multi-source information based remote sensing information model for ground feature:
Most of ground features can not rely on just a single information model. Especially when
coming across the appearance of different objects but equally spectra or equally spectra but
different objects, it is necessary to use multi-temporal remote sensing information or other
supplementary data (supported with GIS data base) to build its corresponding remote sensing
information model for ground features. Based on primitive class model, this model is created by
fresh supplying new element terms according to derivate mechanism.
- Geo-knowledge based remote sensing information model for ground feature:
Some ground features or appearances, especially the recessive information, can be recognized
only through complicated processing and deeply analyzing. This kind of remote sensing
information model for ground features not only includes spectrum vector characteristics of
ground features but also needs to add geo-knowledge and expert knowledge and experience as
well as the process of operating the knowledge and experience. For the reason, the study to this
model deals with wide range of fields and is very complicated.
For main objects such as water, vegetation and non-vegetable, they can be extracted only by
establishment of spectral feature model. For example,
Figure 1 Hierarchy of ground objects
Figure 2 Spectral curve of different objects
