A novel Venus’ visible image processing neoteric workflow for improved planetary surface feature analysis

IF 3.7 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Analysis and Applications Pub Date : 2024-03-12 DOI:10.1007/s10044-024-01253-4

Indranil Misra, Mukesh Kumar Rohil, SManthira Moorthi, Debajyoti Dhar

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Abstract

The article presents a novel methodology that comprises of end-to-end Venus’ visible image processing neoteric workflow. The visible raw image is denoised using Tri-State median filter with background dark subtraction, and then enhanced using Contrast Limited Adaptive Histogram Equalization. The multi-modal image registration technique is developed using Segmented Affine Scale Invariant Feature Transform and Motion Smoothness Constraint outlier removal for co-registration of Venus’ visible and radar image. A novel image fusion algorithm using guided filter is developed to merge multi-modal Visible-Radar Venus’ image pair for generating the fused image. The Venus’ visible image quality assessment is performed at each processing step, and results are quantified and visualized. In addition, fuzzy color-coded segmentation map is generated for crucial information retrieval about Venus’ surface feature characteristics. It is found that Venus’ fused image clearly demarked planetary morphological features and validated with publicly available Venus’ radar nomenclature map.

Graphical abstract

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用于改进行星表面特征分析的新型金星可见光图像处理新工作流程

文章介绍了一种新颖的方法，包括端到端维纳斯可见光图像处理新技术工作流程。利用三态中值滤波器对可见光原始图像进行去噪处理，并减去背景暗部，然后利用对比度受限的自适应直方图均衡化技术进行增强。利用分段仿射尺度不变特征变换和运动平滑约束离群点去除技术，开发了多模态图像配准技术，用于金星可见光图像和雷达图像的共同配准。利用引导滤波器开发了一种新型图像融合算法，用于合并金星可见光-雷达多模态图像对，生成融合图像。在每个处理步骤中都对金星可见光图像质量进行评估，并将结果量化和可视化。此外，还生成了模糊彩色编码分割图，用于检索有关金星表面特征的重要信息。研究发现，金星融合图像清晰地标示了行星形态特征，并与公开的金星雷达命名图进行了验证。

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来源期刊

Pattern Analysis and Applications 工程技术-计算机：人工智能

CiteScore

7.40

自引率

2.60%

发文量

审稿时长

13.5 months

期刊介绍： The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.