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Currently, the use of deep learning for detecting ISWs in synthetic aperture radar (SAR) imagery is gaining growing attention. However, these approaches often demand a considerable number of labeled images, which can be challenging to acquire in practice. In this study, we propose an innovative method employing a pyramidal conditional generative adversarial network (PCGAN). At each scale, it employs the framework of a conditional generative adversarial network (CGAN), comprising a generator and a discriminator. The generator works to produce internal wave patterns as authentically as possible, while the discriminator is designed to differentiate between images generated by the generator and reference images. The architecture based on pyramids adeptly captures the encompassing as well as localized characteristics of internal waves. The incorporation of upsampling further bolsters the model\u2019s ability to recognize fine-scale internal wave stripes. These attributes endow the PCGAN with the capacity to learn from a limited amount of internal wave observation data. Experimental results affirm that the PCGAN, trained with just four internal wave images, can accurately detect internal wave stripes in the test set. Through comparative experiments with other segmentation models, we demonstrate the effectiveness and robustness of PCGAN.<\/jats:p>","DOI":"10.3390\/rs16050787","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T10:40:17Z","timestamp":1708944017000},"page":"787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9510-0764","authenticated-orcid":false,"given":"Bohuai","family":"Duan","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Inner Mongolia Normal University, Huhhot 010028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-1452","authenticated-orcid":false,"given":"Saheya","family":"Barintag","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Inner Mongolia Normal University, Huhhot 010028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3358-8245","authenticated-orcid":false,"given":"Junmin","family":"Meng","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-8556","authenticated-orcid":false,"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1146\/annurev.fl.11.010179.002011","article-title":"Internal waves in the ocean","volume":"11","author":"Garrett","year":"1979","journal-title":"Annu. 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