@inproceedings{e8d9187e6fe84c3babda214becfabc3d,
title = "Hyperspectral Image Classification of Tree Species with Low-Depth Features",
abstract = "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.",
keywords = "Conv2DLSTM, Tree species, high-level features, low-level features",
author = "Zhengqi Guo and Mengmeng Zhang and Wen Jia and Wei Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10282457",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7571--7574",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}