How Much Wavelet Decomposition can Improve the Detection of Surface Fractures in Remote Sensing Images?

E. Souza, Ademir Marques, Rafael Kenji Horota, L. S. Kupssinskü, Pedro Rossa, A. S. Aires, L. G. D. Silveira, M. Veronez, C. Cazarin
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引用次数: 1

Abstract

In this paper we propose a new approach to automatically detect and extract fractures as well as estimate the aperture measures from orbital/aerial images. We show, in a first step, the capability of a translation invariant wavelet multiscale decomposition (NDWT) to separate the information related to the fractures from other features in the image. In the second stage, the aperture size (widths of the fractures) in different positions are estimated across scales using curvature analysis. In a third step, the fractures can be automatically extracted using a growing algorithm. Besides the measures of the fracture apertures in an image of Thingvellir in Iceland, we showed the correlation between the fractures of interest extracted automatically and the respective extracted manually were high (0.9) while only 51 % of then were extracted using just curvature analysis and growing algorithm (without NDWT).
小波分解能在多大程度上改善遥感图像表面裂缝的检测?
本文提出了一种从轨道/航空图像中自动检测和提取裂缝以及估计裂缝孔径的新方法。在第一步中,我们展示了平移不变小波多尺度分解(NDWT)从图像中的其他特征中分离与裂缝相关信息的能力。在第二阶段,利用曲率分析跨尺度估计不同位置裂缝的孔径大小(裂缝宽度)。第三步,使用生长算法自动提取裂缝。除了冰岛Thingvellir的裂缝孔径测量外,我们还发现自动提取的裂缝与人工提取的裂缝之间的相关性很高(0.9),而仅使用曲率分析和生长算法(没有NDWT)提取的裂缝仅占51%。
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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