{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:22:00Z","timestamp":1781846520647,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2021376"],"award-info":[{"award-number":["2021376"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared small target detection (IRSTD) is the process of recognizing and distinguishing small targets from infrared images that are obstructed by crowded backgrounds. This technique is used in various areas, including ground monitoring, flight navigation, and so on. However, due to complex backgrounds and the loss of information in deep networks, infrared small target detection remains a difficult undertaking. To solve the above problems, we present a shallow feature fusion network (SFFNet) based on detection framework. Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. Then, we design the visual-Mamba-based global information extension (VMamba-GIE) module, which leverages a multi-branch structure combining the capability of convolutional layers to extract features in local space with the advantages of state space models in the exploration of long-distance information. The design significantly extends the network\u2019s capacity to acquire global contextual information, enhancing its capability to handle complex backgrounds. And through the effective fusion of the SLGFE and VMamba-GIE modules, the exorbitant computation brought by the SLGFE module is substantially reduced. The experimental results on two publicly available infrared small target datasets demonstrate that the SFFNet surpasses other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/rs16224160","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T04:02:54Z","timestamp":1731038574000},"page":"4160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhihui","family":"Yu","sequence":"first","affiliation":[{"name":"National Laboratory on Adaptive Optics, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nian","family":"Pan","sequence":"additional","affiliation":[{"name":"National Laboratory on Adaptive Optics, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Laboratory on Adaptive Optics, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MGRS.2022.3145502","article-title":"Single-Frame Infrared Small-Target Detection: A survey","volume":"10","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. 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