{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:05:41Z","timestamp":1760231141419,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"]}]},{"name":"National Key Research and Development Program of China","award":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"]}]},{"name":"Natural Science Foundation of Chongqing","award":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","62027827","2019YFE0110800","cstc2020jcyj- zdxmX0025","cstc2019cxcyljrc-td0270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pansharpening methods based on deep learning can obtain high-quality, high-resolution multispectral images and are gradually becoming an active research topic. To combine deep learning and remote sensing domain knowledge more efficiently, we propose a double-stack aggregation network using a feature-travel strategy for pansharpening. The proposed network comprises two important designs. First, we propose a double-stack feature aggregation module that can efficiently retain useful feature information by aggregating features extracted at different levels. The module introduces a new multiscale, large-kernel convolutional block in the feature extraction stage to maintain the overall computational power while expanding the receptive field and obtaining detailed feature information. We also introduce a feature-travel strategy to effectively complement feature details on multiple scales. By resampling the source images, we use three pairs of source images at various scales as the input to the network. The feature-travel strategy lets the extracted features loop through the three scales to supplement the effective feature details. Extensive experiments on three satellite datasets show that the proposed model achieves significant improvements in both spatial and spectral quality measurements compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14174224","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Double-Stack Aggregation Network Using a Feature-Travel Strategy for Pansharpening"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9033-8245","authenticated-orcid":false,"given":"Weisheng","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Maolin","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Minghao","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2021.10.001","article-title":"A theoretical and practical survey of image fusion methods for multispectral pansharpening","volume":"79","author":"Yilmaz","year":"2022","journal-title":"Inf. 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