Methods of obtaining geospatial data using satellite communications and their processing using convolutional neural networks
The availability of high-resolution satellite images obtained through space radio communications offers the opportunity to use the most advanced technologies and techniques for analyzing remote sensing data. The paper discusses the data obtained with the use of ground-based, airborne or space-based filming equipment, which makes it possible to obtain images in one or several sections of the electromagnetic spectrum. This article provides an overview of existing artificial spacecraft and systems for obtaining space data. Also, there are the examples of the use of convolutional neural networks (CNN) for processing data obtained from artificial Earth satellites. CNN has a high learning ability and the capacity to automatically learn optimal functions based on the data.
A novel method for evaluating classification reliability is proposed based on the discernibility of a pattern’s class against other classes from the pattern’s location. Use of three measures of discernibility is experimentally compared with conventional techniques based on the classification scores for class labels. The classification accuracy can be drastically enhanced through discernibility measures by using the most reliable – “elite” – patterns. It can be further boosted by forming an amalgamation of the elites of different classifiers. Improved performance is achieved at the price of rejecting many patterns. There are situations where this price is worth paying – when the non-reliable accuracy rates lead to the need in manually testing of very complex technical devices or in diagnostics of human diseases. Contrary to conventional techniques for estimating reliability, the proposed measures are applicable on small datasets as well as on datasets with complex class structures where conventional classifiers show low accuracy rates.
This book constitutes the refereed proceedings of the 9th International Conference on Cellular Automata for Research and Industry, ACRI 2010, held in Ascoli Piceno, Italy, in September 2010. The first part of the volume contains 39 revised papers that were carefully reviewed and selected from the main conference; they are organized according to six main topics: theoretical results on cellular automata, modeling and simulation with cellular automata, CA dynamics, control and synchronization, codes and cryptography with cellular automata, cellular automata and networks, as well as CA-based hardware. The second part of the volume comprises 35 revised papers dedicated to contributions presented during ACRI 2010 workshops on theoretical advances, specifically asynchronous cellular automata, and challenging application contexts for cellular automata: crowds and CA, traffic and CA, and the international workshop of natural computing.
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on counter-examples and discuss them in terms of pattern structures. We show how such classifiers are related. In particular, we discuss the equivalence between good maximally redundant tests and minimal JSM-hyposethes and between minimal representations of version spaces and good irredundant tests.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
Studied is a possibility of increasing the accuracy of diagnostics by examining a number of diagnostic rules as a set of expert assessments, which allows one to combine them («mix of expert opinions»). Proposed is to use of the principle of minimum-information-mismatch in Kullback - Leibler metric to highlight the rule most appropriate for classification of a particular object. Program and results of experimental study are presented in the problem of automatic recognition of gray-scale images. It is shown that the developed approach can significantly improve the quality of diagnostics.