Change detection techniques

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

Перевод пока недоступен