Urban Land Chang Detection on Remote Sensing Images Based on Local Similarity Siamese Network

Abstract

Lloyd created the well-known signal quantization issue. We define a different, related problem: The best translation of digital fine grayscale images to a coarser scale (for example, medical imaging with 9–13 bits per pixel) (for instance, 8 bits per pixel on standard computer displays). Although the latter pertains to a mostly digital domain, the former problem is specified primarily in the actual signal domain with smoothly distributed noise. The conventional quantization methods are essentially inapplicable non typical scenarios of quantization of the previously digitised pictures, as we demonstrate in this study, due to this discrepancy. Through experimentation, we discovered that Lloyd's technique is greatly outperformed by a dynamic programming-based solution. The maintenance of any picture database must have two fundamental elements: data representation and content description. In this study, a wavelet-based system called the Waveguide is suggested, which unifies these two elements into a single framework. In this study, a unique way of rating the differences between two satellite photos obtained at various times is presented by this system for unsupervised change analysis. Change Vector Analysis Technique was employed in the current system of change analysis. The polar CVA representation serves as the foundation for this system. In the suggested method of change analysis, the Hamming distance, which is predicated upon binary descriptors, is utilised as a similarity metric.