Please use this identifier to cite or link to this item:
|Title:||Classification of multi-temporal SAR images and Insar coherence images using adaptive neighborhood model and simulated annealing approach||Authors:||Bao, M.||Keywords:||Adaptive neighborhood model
Classification, Fuzzy c-mean clustering
InSAR coherence image
Multi-temporal SAR images
Possibility c-mean clustering
|Issue Date:||2000||Citation:||Bao, M. (2000). Classification of multi-temporal SAR images and Insar coherence images using adaptive neighborhood model and simulated annealing approach. European Space Agency, (Special Publication) ESA SP (461) : 811-816. ScholarBank@NUS Repository.||Abstract:||A classification algorithm for multi-temporal SAR images and InSAR coherence images is developed. This is done by minimizing an energy function which is defined by the sum of (1) the difference between the pixel value and the cluster center (mean value of a class) and (2) the difference between the pixel label (class) and its neighborhood. The global minimization of the energy function of the whole image is achieved by a simulated annealing approach. The cluster centers for different classes are calculated using fuzzy c-mean and possibility c-mean algorithms or determined manually from the image values. In order to preserve the details of the original images, a neighborhood structure with a 5*1 window is used to calculate the energy function, whose orientation is determined by the local homogeneity (e.g. the variance of the pixel values within the window) of the original images. This algorithm is used to classify three ERS-2 SAR images over the Mekong delta region, Vietnam. By comparing visually the color composite SAR images and the classified image, it is shown that the classification result is reasonable. Moreover, this algorithm is also used to classify an InSAR coherence image acquired from two JERS SAR images over south Sumatra, Indonesia. The classified InSAR coherence image delineates the bare land and forest clearly, since the coherence of bare land is larger than that of forest.||Source Title:||European Space Agency, (Special Publication) ESA SP||URI:||http://scholarbank.nus.edu.sg/handle/10635/112855||ISSN:||03796566|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 23, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.