计算机科学
人工智能
特征(语言学)
模式识别(心理学)
特征提取
高光谱成像
激光雷达
遥感
哲学
语言学
地质学
作者
Jiaojiao Li,Yuzhe Liu,Rui Song,Liu Wei,Yunsong Li,Qian Du
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:62: 1-14
标识
DOI:10.1109/tgrs.2024.3355037
摘要
Hyperspectral images have excellent spectral combining capabilities and LiDAR images have fine stereooscopic elevation information. Therefore, the multi-modal fusion classification of hyperspectral and LiDAR images is inevitably improves the interpretation ability of remote sensing images. In recent years, the MLP-Mixer, an image processing network based on MLP, has flourished in the field of image processing. In this work, we propose an innovative HyperMLP network based on the deep learning framework MLP-Mixer architecture to address the lack of spatial feature construction capability and the locality of multi-modal feature fusion in naive networks. Specifically, (1) The adoption of unsupervised superpixel embedding provides additional shallow morphological spatial feature information for the network, reduces the pressure of the feature extraction network, and enhances feature discrimination capabilities. (2) The feature scrambling strategy improves the diversity of features and strengthens generalization of the network by enhancing interactions between different spatial features. (3) By implementing the bilateral modulation strategy, feature fusion is applied at every stage of the deep network, reducing semantic drift between features . On three fiducial remote sensing datasets, classification tests are performed on the proposed HyperMLP network to verify its performance, and the results are definitely impressive.
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