文献类型：期刊文献

英文题名：Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

作者：Guo, Zhengqi[1] Zhang, Mengmeng[1] Jia, Wen[2] Zhang, Jinxin[1] Li, Wei[1]

第一作者：Guo, Zhengqi

通信作者：Zhang, MM[1];Jia, W[2]

机构：[1]Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2022

卷号：15

起止页码：7013-7024

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：;EI(收录号：20223512672189);Scopus(收录号：2-s2.0-85136864408);WOS:【SCI-EXPANDED(收录号:WOS:000849263100005)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62001023 and Grant 42101403, in part by the China Postdoctoral Science Foundation under Grant 2021M700441, and in part by the National Key Research and Development Program of China under Grant 2019YFE0126700.

语种：英文

外文关键词：Vegetation; Hyperspectral imaging; Feature extraction; Forestry; Data mining; Morphology; Laser radar; Dual-concentrated network with morphological features (DNMF); hyperspectral image (HSI); mathematical morphology; tree species

摘要：At present, deep learning is a hot topic in the field of the classification of hyperspectral image (HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such as tree species classification, the uncertain spectrum remains the major factor restraining the classification performance. In order to solve the dilemma of forest tree species classification, a dual-concentrated network with morphological features (DNMF) is proposed. First, mathematical morphology is used to extract the morphological features of HSI. Then, coarse-grained information is extracted from the original hyperspectral data, and fine-grained information is extracted from morphological features. After that, both morphological representations and spectral inputs are fed into DNMF, and the overall evaluation index and visual image are obtained. The advantage of DNMF is that it decouples the spatial and spectral information, and a multisource information fusion process is then simulated. Accordingly, DNMF obtains high tree species classification accuracy. In order to verify the superiority of DNMF, we choose Gaofeng State-owned Forest Farm in Guangxi Province and the Belgium dataset, which was collected near the western part of Belgium as the research area. Related experiments demonstrate that the DNMF model achieves clearly better classification performance over other competitive baselines.