Urban change detection based on an artificial neural network

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Abstract
A method based on an artificial neural network (ANN) was developed to detect newly urbanized areas depicted in satellite sensor images. The method uses two Landsat Thematic Mapper (TM) images of a region acquired on different dates as input and supervises the ANN to classify the image data into 'from-to' classes. Principal component analysis (PCA) was applied to extract the salient features and to reduce the dimensionality of the input data prior to the ANN-based change detection. The Levenburg-Marquardt algorithm was used to accelerate the ANN's convergence. Experimental results from a case study show the ANN-based method requires only modest training time but can be 20-30% more accurate than post-classification comparison. PCA not only reduced the computational cost but improved the change detection accuracy as well. The results suggest the practical value of ANN-based change detection.