Interactive Transformer and Cnn Network for Fusion Classification of Hyperspectral and Lidar Data

Author:

Wang Leiquan,Liu Wenwen,Lv Dong,Zhang Peiying,Guo Fangming,Hu Yabin,Xu Mingming

Publisher

Elsevier BV

Reference40 articles.

1. More diverse means better: Multimodal deep learning meets remote-sensing imagery classification;D Hong;IEEE Transactions on Geoscience and Remote Sensing,2020

2. Deep neural networks-based relevant latent representation learning for hyperspectral image classification;A Sellami;Pattern Recognition,2022

3. Joint classification of hyperspectral and lidar data using height information guided hierarchical fusion-andseparation network;T Song;IEEE Transactions on Geoscience and Remote Sensing,2024

4. Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging;T Qiao;Pattern Recognition,2018

5. Remotely sensed image classification using sparse representations of morphological attribute profiles;B Song;IEEE transactions on geoscience and remote sensing,2013

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