Abstract
Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.
Abbreviations used in this paper
6S second simulation of the satellite signal in the solar spectrum
ANN artificial neural networks
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer
AVHRR Advanced Very High Resolution Radiometer
AVIRIS Airborne Visible/Infrared Imaging Spectrometer
CVA change vector analysis
EM expectation–maximization algorithm
ERS-1 Earth Resource Satellite-1
ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image
GIS Geographical Information System
GS Gramm–Schmidt transformation
J-M distance Jeffries–Matusita distance
KT Kauth–Thomas transformation or tasselled cap transformation
LSMA linear spectral mixture analysis
LULC land use and land cover
MODIS Moderate Resolution Imaging Spectroradiometer
MSAVI Modified Soil Adjusted Vegetation Index
MSS Landsat Multi-Spectral Scanner image
NDMI Normalized Difference Moisture Index
NDVI Normalized Difference Vegetation Index
NOAA National Oceanic and Atmospheric Administration
PCA principal component analysis
RGB red, green and blue colour composite
RTB ratio of tree biomass to total aboveground biomass
SAR synthetic aperture radar
SAVI Soil Adjusted Vegetation Index
SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image
TM Thematic Mapper
VI Vegetation Index
Acknowledgments
The authors wish to thank the National Science Foundation (grants 95-21918 and 99-06826) and the National Aeronautics and Space Administration (grant N005-334) for their support. The authors also would like to thank the anonymous reviewers for their comments and suggestions to improve this paper.