{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T16:04:05Z","timestamp":1784649845508,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Project of Jiangxi Provincial Department of Education","award":["GJJ2200421"],"award-info":[{"award-number":["GJJ2200421"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of remote sensing technology, high-resolution satellite imagery has been increasingly applied to land resource monitoring, urban planning, and environmental assessment. Automatically assigning semantic labels to remote sensing image patches remains a fundamental challenge due to pronounced intra-class variation and high inter-class visual similarity. To address the trade-off between model capacity and limited labeled data, this paper proposes a remote sensing image scene classification framework based on an improved EfficientNetV2-S architecture. The proposed model integrates a Squeeze-and-Excitation (SE) channel attention module between the final 1 \u00d7 1 expansion convolution and the Global Average Pooling layer, where it functions as a late-stage channel gating mechanism that adaptively recalibrates channel-wise responses, though its accuracy benefit is seed-sensitive rather than consistently reproducible at the current dataset scale. A two-stage optimization strategy was evaluated, comprising a fully unfrozen supervised baseline followed by a pseudo-label semi-supervised fine-tuning stage utilizing a strict confidence threshold (\u03c4=0.90). Evaluated on a 10-class subset of the public NWPU-RESISC45 benchmark, the purely supervised SE-EfficientNetV2-S delivers 98.71% independent test accuracy, matching or exceeding the much larger ResNet50 (98.50%, 24.1 M parameters) despite using only 20.4 M parameters. Multi-seed variance analysis further reveals that semi-supervised fine-tuning yields a small test-set improvement for the No-SE configuration that is consistent in sign across all three seeds (+0.46 pp mean) but not statistically significant at this sample size, and an even smaller, likewise non-significant gain for the SE-augmented model (+0.08 pp), suggesting that channel gating moderates pseudo-label effectiveness in small-data regimes.<\/jats:p>","DOI":"10.3390\/s26144617","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:17:52Z","timestamp":1784647072000},"page":"4617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remote Sensing Image Scene Classification with SE-EfficientNetV2-S: An Empirical Study of Channel Attention and Semi-Supervised Pseudo-Labeling"],"prefix":"10.3390","volume":"26","author":[{"given":"Liting","family":"Liao","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6340-2689","authenticated-orcid":false,"given":"Haoyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"},{"name":"Key Laboratory of Agricultural Information Technology of Higher Education Institutions of Jiangxi Province, Nanchang 330045, China"},{"name":"Nanchang Digital Agriculture and Intelligent Perception Collaborative Innovation Key Laboratory, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"},{"name":"Key Laboratory of Agricultural Information Technology of Higher Education Institutions of Jiangxi Province, Nanchang 330045, China"},{"name":"Nanchang Digital Agriculture and Intelligent Perception Collaborative Innovation Key Laboratory, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runqiu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","first-page":"3768","article-title":"A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends","volume":"12","author":"Younesi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Li, J., Zhang, Y., Gong, W., and Zhang, Z. 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