Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2021-03-30 DOI:10.15622/IA.2021.20.2.3
V. Nenashev, I. Khanykov
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引用次数: 4

Abstract

The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series. The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images. The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them. The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.  
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基于多位置机载系统中位置图像像素聚类的陆地表面融合图像的形成
本文提出了一种多角度图像融合方法,实现了像素准最优聚类算法与地表原始图像的融合。将多位置定位系统机载设备形成的多角度原始图像对接成单幅合成图像,利用准最优像素聚类的高速算法,在保持特征边界的前提下,将图像缩减为几种颜色。准最优像素聚类算法的一个特点是,由于聚类数量的变化,生成了一系列细节逐渐增加的分区。该特性允许您从生成的序列中选择一对停靠映像的适当分区。对停靠图像的选定分区中的一对图像进行隔离轮廓参考点的搜索。对于这些点确定一个函数变换。将其应用于原始图像后,估计融合图像的相关程度。轮廓参考点的位置和期望的功能变换本身都被细化,直到融合质量的评价是可以接受的。根据颜色还原后的图像选择函数变换的类型,然后将其应用于原始图像。如果对融合质量的评估是不可接受的,则对更详细的聚类图像重复此过程。本研究的目的是开发一种从异质影像和异质影像合成地表融合影像的方法。本文介绍了该融合方法的以下特点。第一个特征是通过像素聚类算法从一对停靠的源图像中处理单个合成图像,这使得以类似的方式分离不同部分的相同区域成为可能。第二个特征是通过处理后的聚类图像对上轮廓的孤立参考点确定函数变换,然后将其应用于原始图像进行组合。本文介绍了从均匀(光学)图像和非均匀(雷达和光学)图像合成融合图像的结果。该方法的一个显著特点是提高了合成质量,提高了最终融合的地表图像的精度和信息量。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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