Detection System of Landscape’s Unnatural Changes by Satellite Images Based on Local Areas
IF 0.7
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
引用次数: 0
Abstract
The paper deals with a state-of-the-art applied problem related to the detection of landscape’s unnatural changes based on satellite images. An approach to constructing a detection system based on neural network processing of local terrain areas is proposed. As part of the approach, a neural network architecture and mechanisms for tuning to a specific area have been developed. It is shown that the use of neural networks and images corresponding to local areas (as initial data) provides easy expansion of the system to various types of terrain. The paper also presents a data filtering algorithm to adjust the balance of recall and overall precision of the system. Experimental studies have confirmed the effectiveness of the proposed approach.
基于本地区域的卫星图像景观非自然变化检测系统
摘要 本文论述了一个与基于卫星图像的景观非自然变化检测有关的最新应用问题。本文提出了一种基于神经网络处理局部地形区域的检测系统构建方法。作为该方法的一部分，开发了一种神经网络结构和机制，用于调整特定区域。结果表明，使用神经网络和与局部区域相对应的图像（作为初始数据），可以很容易地将系统扩展到各种类型的地形。论文还提出了一种数据过滤算法，用于调整系统的召回率和总体精度之间的平衡。实验研究证实了拟议方法的有效性。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍：
The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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