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Performance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching
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Negar Jovhari *   , Amin Sedaghat , Nazila Mohammadi |
| University of Tabriz |
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Abstract: (1588 Views) |
| Automatic, efficient, accurate, and stable image matching is one of the most critical issues in remote sensing, photogrammetry, and machine vision. In recent decades, various algorithms have been proposed based on the feature-based framework, which concentrates on detecting and describing local features. Understanding the characteristics of different matching algorithms in various applications increases the potential of successful matching in a given application. Numerous studies have evaluated and analyzed many of these algorithms in various applications. However, performance evaluation of image matching methods in multi-sensor images, especially optical-radar and noisy images, is limited. This research will evaluate the performance of the state-of-the-art- detectors, including SURF, KAZE, SIFT, PC, FAST, and Harris detectors for multi-sensor image matching. Moreover, we integrated the employed detectors with the uniform competency algorithm to identify the most reliable features with uniform distribution. Next, we employed a scale-invariant version of the HOSS descriptor to describe extracted features. The results show the superiority of the KAZE detector in the presence of noise and various geometric and radiometric distortions. |
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| Keywords: Multimodal images, evaluation of local detectors, KAZE Detector, HOSS descriptor, Uniform Competency method |
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Full-Text [PDF 2332 kb]
(396 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2022/01/2 | Accepted: 2022/06/6 | Published: 2022/11/1
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