文献类型：期刊文献

英文题名：Hyperspectral Image Classification of Tree Species with Low-Depth Features

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

第一作者：Guo, Zhengqi

机构：[1] Beijing Institute of Technology, School of Information and Electronics, Beijing, China; [2] Chinese Academy of Forestry, Institute of Forest Resource Information Techniques, Beijing, China

年份：2023

卷号：2023-July

起止页码：7571-7574

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20234815128994)

语种：英文

摘要：Classification of tree species is of great significance to forest surveys. Recently, considering the low differences of spectral information among tree species, enhancing the dependence between long-distance bands has become a research hotspot. A tree species classification method based on a convolutional (2-dimension) long short-term memory (Conv2DLSTM) network and transformer is proposed. First, the main features of HSI are retained by principal component analysis (PCA). Then, the Conv2DLSTM network obtains the global correlation information between long-distance band pixels, and the 3-dimensional convolutional neural network (3DCNN) updates the local spatial-spectral information. Finally, low-level features are converted into semantic tags to guide the modeling of high-level semantic features. The experimental results on the forest dataset demonstrate that the proposed method is superior to other competitive work. ? 2023 IEEE.