Impressions of footwear are commonly found in crime scenes. The quality and wide variability of these impressions makes their analysis is very difficult. This research will develop new computational methods to assist the forensic footwear examiner in the U.S. In this research work involves developing a database of representative footwear print images so that appropriate algorithms can be developed and their error rates can be determined. Algorithms for identifying special features such as wear marks and embedded pebbles will be developed. Matching algorithms to be developed will be for both the tasks of verification, where the goal is to determine whether the footwear evidence is from a particular suspect’s shoe, or that of identification, where the goal is to determine the brand of the shoe from a known set of brands. In each case a quantitative measure of the result of matching will be provided. In the identification mode, the tools will allow the narrowing down of possibilities in a database of known prints. Another goal this work is to assist the U.S. footwear examiner is homicides and assaults where there are no known prints to match. For this purpose a classification tool is to be developed, where the objective is to generate from the evidence a set of characteristics, e.g., gender, texture, shape, size and brand. This work will be extended by following guidelines of SWGTREAD and in close consultation with forensic footwear and/or tire tread examiners.

This paper describes the MLP NN classifier performing optimally in classifying the different land types from Landsat data. In fact classification process is a compulsory step in any remote sensing research. Therefore, the main objective of this paper is to assess classification accuracy of classified lands on benchmark Landsat data from UCI machine learning repository. The six land type classes namely red soil, cotton crop soil, damp grey soil, soil with vegetation stubble, very damp grey soil can be Identified. Result showed that overall classification accuracy is 87.57%, which is considered acceptable. Results show that this new neural network model is more accurate than the other NN models. These results suggest that this model is effective for classification of satellite image data.