Abstract
ABSTRACT Automatic change detection in remote sensing images of a specified image acquired at various time period, which is unique and fascinating idea of image processing. The significance of change-detection techniques depends on probability of recognizing the changes in land covers by investigating multispectral images acquired at different session. Based on the previous papers accurate change detection (CD) is not yet identified. Enhanced Back-Propagation Neural Network (EBPNN) technique for CD is used to enhance the accuracy based on many existing techniques. This method involves an Adaptive Median Filter (AMF) that is responsible to remove the noise identified in remote sensing images. Moreover, feature extraction is achieved with the Tamura and Law’s mask features by which minute features are extorted from the images. By contrasting numerous existing techniques, the precision is measured with the support of different parameters.
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