{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:40:53Z","timestamp":1764175253591,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Changsha Municipal Natural Science Foundation","award":["kq2014160","61703441","19A511","2019TP1015"],"award-info":[{"award-number":["kq2014160","61703441","19A511","2019TP1015"]}]},{"name":"National Natural Science Foundation in China","award":["kq2014160","61703441","19A511","2019TP1015"],"award-info":[{"award-number":["kq2014160","61703441","19A511","2019TP1015"]}]},{"name":"Department of Education Hunan Province","award":["kq2014160","61703441","19A511","2019TP1015"],"award-info":[{"award-number":["kq2014160","61703441","19A511","2019TP1015"]}]},{"name":"Hunan Key Laboratory of Intelligent Logistics Technology","award":["kq2014160","61703441","19A511","2019TP1015"],"award-info":[{"award-number":["kq2014160","61703441","19A511","2019TP1015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network\u2019s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network\u2019s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images.<\/jats:p>","DOI":"10.3390\/rs14153684","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T21:01:24Z","timestamp":1659387684000},"page":"3684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters"],"prefix":"10.3390","volume":"14","author":[{"given":"Zongchen","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Ruoli","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6795-6152","authenticated-orcid":false,"given":"Weiwei","family":"Cai","sequence":"additional","affiliation":[{"name":"Graduate College, Northern Arizona University, P.O. Box 4084, Flagstaff, AZ 86011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3733-631X","authenticated-orcid":false,"given":"Yongfei","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Yaowen","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4245-3628","authenticated-orcid":false,"given":"Liujun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural and Environmental Engineering, University of Missouri-Rolla, Rolla, MO 65401, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1191\/0309133305pp432ra","article-title":"Satellite remote sensing of forest resources: Three decades of research development","volume":"29","author":"Boyd","year":"2005","journal-title":"Prog. 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