Feature-based image fusion scheme for satellite recognition

Han Pan, G. Xiao, Zhongliang Jing
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引用次数: 4

Abstract

Despite the variety of technologies and algorithms studied, satellite recognition is not fully researched in the uncontrolled space environments. In this paper, a low complexity and efficient satellite recognition scheme by fusing infrared and visible image features for recognition was brought forward. Invariant moments are taken to represent the characteristics of satellites' pictures. Unlike optimal image feature fusion by classic intelligent computing algorithms, a low computation and efficient fusion rules are developed to improve the performance of recognition. Due to the compute power of space-based computer, a new fusion method by associating combined blur and affine moments invariant (CBAI) with Zernike moments is introduced. The experiments results with Semi-physical simulation images indicate that the recognition consistently demonstrated better performance than others solely based on either infrared or visible image.
基于特征的图像融合卫星识别方案
尽管研究了各种各样的技术和算法,但在不受控制的空间环境中对卫星识别的研究并不充分。本文提出了一种低复杂度、高效的融合红外和可见光图像特征的卫星识别方案。采用不变矩表示卫星图像的特征。与传统智能计算算法的图像特征融合优化不同,本文提出了一种计算量小、效率高的融合规则来提高识别性能。基于天基计算机的计算能力,提出了一种将模糊和仿射组合矩不变量(CBAI)与Zernike矩相关联的融合方法。半物理模拟图像的实验结果表明,该方法的识别效果始终优于单纯基于红外或可见光图像的识别方法。
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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