{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:25:53Z","timestamp":1766586353011,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32171779","2021YFE0117700","59257"],"award-info":[{"award-number":["32171779","2021YFE0117700","59257"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["32171779","2021YFE0117700","59257"],"award-info":[{"award-number":["32171779","2021YFE0117700","59257"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dragon 5 Cooperation","award":["32171779","2021YFE0117700","59257"],"award-info":[{"award-number":["32171779","2021YFE0117700","59257"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median\u2013Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management.<\/jats:p>","DOI":"10.3390\/rs16020416","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T06:49:31Z","timestamp":1705906171000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SA-Pmnet: Utilizing Close-Range Photogrammetry Combined with Image Enhancement and Self-Attention Mechanisms for 3D Reconstruction of Forests"],"prefix":"10.3390","volume":"16","author":[{"given":"Xuanhao","family":"Yan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Guoqi","family":"Chai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Xinyi","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Lingting","family":"Lei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Geng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2112-1031","authenticated-orcid":false,"given":"Xiang","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7443-1557","authenticated-orcid":false,"given":"Xiaoli","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chirico, G.B., and Bonavolont\u00e0, F. 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