title: Integration of Gibbs Markov random field and Hopfield-type neural networks for unsupervised change detection in remotely sensed multitemporal images.
abstract: In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India. ash@isical.ac.in
authors.forename: Ashish
authors.initials: A
authors.lastname: Ghosh
authors.forename: Badri Narayan
authors.initials: BN
authors.lastname: Subudhi
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Bruzzone
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D058998:Remote Sensing Technology / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018511:Systems Integration