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Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies.<\/jats:p>","DOI":"10.3390\/rs16111901","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T05:14:02Z","timestamp":1716786842000},"page":"1901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Step Unsupervised Domain Adaptation in Image and Feature Space for Synthetic Aperture Radar Image Terrain Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5425-6437","authenticated-orcid":false,"given":"Zhongle","family":"Ren","sequence":"first","affiliation":[{"name":"The School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Du","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Sha","sequence":"additional","affiliation":[{"name":"High Resolution Earth Observation System Shaanxi Data and Application Center, Xi\u2019an 710061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-8955","authenticated-orcid":false,"given":"Weibin","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Hou","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Two-Stage Cross-Modality Transfer Learning Method for Military-Civilian SAR Ship Recognition","volume":"19","author":"Song","year":"2022","journal-title":"IEEE Geosci. 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