Object Detection with Few Training Data: Detection of Subsiding Troughs in SAR Interferograms-Reference-Cited by-同舟云学术

Object Detection with Few Training Data: Detection of Subsiding Troughs in SAR Interferograms

Published:2017 Issue: Volume: Page:570-579
ISSN:0302-9743
Container-title:Artificial Intelligence and Soft Computing
language:
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Author:

Rotter Paweł,Strzelczyk Jacek,Porzycka-Strzelczyk Stanisława,Feijoo Claudio

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-59063-9_51

Reference17 articles.

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4. Leśniak, A., Porzycka, S.: Comprehensive interpretation of satellite and surface measurements for hazard assessment on mining and post mining areas. Mineral Resour. Manage. 24, 147–159 (2008)

5. Tadeusiewicz, R.: Neural networks in mining sciences – general overview and some representative examples. Arch. Min. Sci. 60, 971–984 (2015)

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1. Automatic Detection of Subsidence Troughs in SAR Interferograms Based on Convolutional Neural Networks;IEEE Geoscience and Remote Sensing Letters;2021-01

2. Automatic Detection of Subsidence Troughs in SAR Interferograms Based on Circular Gabor Filters;IEEE Geoscience and Remote Sensing Letters;2018-06