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Performance Comparison of Matching Cost Functions for High-Quality Sea-Ice Surface Model Generation

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Title
Performance Comparison of Matching Cost Functions for High-Quality Sea-Ice Surface Model Generation
Other Titles
고품질 해빙표면모델 생성을 위한 정합비용함수의 성능 비교 분석
Authors
Kim, Jae-In
Kim, Hyun-cheol
Subject
Other natural science
Keywords
Digital surface model; Sea Ice; aerial image; Matching cost
Issue Date
2018-12
Citation
Kim, Jae-In, Kim, Hyun-cheol. 2018. "Performance Comparison of Matching Cost Functions for High-Quality Sea-Ice Surface Model Generation". Korean Journal of Remote Sensing, 34(6-2): 1251-1260.
Abstract
High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on seaice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized crosscorrelation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.
URI
https://repository.kopri.re.kr/handle/201206/10491
DOI
http://dx.doi.org/10.7780/kjrs.2018.34.6.2.9
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