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Research Article

An efficient and accurate deep learning method for tree species classification that integrates depthwise separable convolution and dilated convolution using hyperspectral data

Mengni Fua Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaView further author information
,
Chi Lua Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0003-2125-6104View further author information
ORCID Icon,
Yingwu Maoc Southwest Survey and Planning Institute of National Forestry and Grassland Administration, Kunming, People’s Republic of ChinaView further author information
,
Xiaoli Zhanga Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0003-0768-9595View further author information
ORCID Icon,
Yong Wua Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0001-6343-4218View further author information
ORCID Icon,
Hongbin Luoa Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0002-9885-3014View further author information
ORCID Icon,
Zhi Liua Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaView further author information
,
Wenfang Lia Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaView further author information
&
Guanglong Oua Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming, People’s Republic of China;b Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, People’s Republic of ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1925-6690View further author information
ORCID Icon show all
Article: 2307999 | Received 09 Oct 2023, Accepted 16 Jan 2024, Published online: 23 Jan 2024

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