Frequently, real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform will offer a viable alternative as some early work in the area has indicated. An architecture of an image classification software system is suggested to implement an improved scheme for the analysis, representation, processing and classification of images. The scheme is based on considering the segments of images as wavelets so that small details in the images can be exploited. The objective is to implement this scheme automatically and rapidly decompose a 2D image into a combination of elemental images so that an array of processing methods can be applied. Thus, the scheme offers potential utility for analysis of images and compression of image data. Moreover, the elemental images may be considered patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets.