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

• Published in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium pp. 2619 - 2622
• Main Authors: de Souza, Eniuce Menezes, Marques, Ademir, Horota, Rafael K., Kupssinsku, Lucas, Rossa, Pedro, Aires, Alysson S., Silveira, Luiz Gonzaga, Veronez, Mauricio R., Cazarin, Carol L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26-09-2020
• Subjects: Apertures; Discrete wavelet transforms; Extraterrestrial measurements; Feature extraction; fissures; Geologic measurements; Image reconstruction; lineaments; NDWT; non-decimated wavelet transform; Time-frequency analysis
• DOI: 10.1109/IGARSS39084.2020.9324506
• EISSN: 2153-7003

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).