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Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.<\/jats:p>","DOI":"10.1007\/s40747-023-01294-y","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T05:27:42Z","timestamp":1702963662000},"page":"2851-2868","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7983-4713","authenticated-orcid":false,"given":"Hongjia","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yubin","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yuanshu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Liupu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"You","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"1294_CR1","unstructured":"David Adamy. 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