{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:38:36Z","timestamp":1767339516429,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one.<\/jats:p>","DOI":"10.3390\/rs11242980","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T11:06:41Z","timestamp":1576148801000},"page":"2980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["TanDEM-X Forest Mapping Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Antonio","family":"Mazza","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1593-1492","authenticated-orcid":false,"given":"Francescopaolo","family":"Sica","sequence":"additional","affiliation":[{"name":"Microwaves and Radar Institute at the German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"given":"Paola","family":"Rizzoli","sequence":"additional","affiliation":[{"name":"Microwaves and Radar Institute at the German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6458-9107","authenticated-orcid":false,"given":"Giuseppe","family":"Scarpa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st century forest coverage change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.5721\/EuJRS20144708","article-title":"Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data","volume":"47","author":"Machala","year":"2014","journal-title":"Eur. 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