Construction of a Semiautomatic Contour of Areal Objects on Hyperspectral Satellite Images

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI:10.1134/s1054661824700111

Bin Lei, Wei Wan, Artiom Nedzved, Alexei Belotserkovsky

引用次数: 0

Abstract

In this article, we formalize the problem of semiautomatic construction of the contour of area objects from satellite hyperspectral images and present a solution algorithm using PCA and Dijkstra’s algorithm. The contour is considered as the boundary of an object, which can be used for its segmentation and classification. The semiautomatic contour accepts reference points specified by the operator. The formalization of the algorithm is completed.

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在高光谱卫星图像上构建半自动等值线的区域物体

摘要本文正式提出了从卫星高光谱图像中半自动构建区域物体轮廓的问题，并介绍了一种使用 PCA 和 Dijkstra 算法的求解算法。轮廓被视为物体的边界，可用于物体的分割和分类。半自动轮廓接受操作者指定的参考点。算法的形式化已经完成。

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

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.