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More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

September 24, 2020

More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification
Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du and Bing Zhang

IEEE Transactions on Geoscience and Remote Sensing
year : 2020
DOI: 10.1109/TGRS.2020.3016820

codes : https://github.com/danfenghong/IEEE_TGRS_MDL-RS

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#	File Type	File Size	Download
1	.pdf	11.32 MB	2020_TGRS_Multimodal

Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Graph Convolutional Networks for Hyperspectral Image Classification
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

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