home/Knowledge Base/CODES/Deep Learning/More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

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

Attached Files

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

An Offset Graph U-Net for Hyperspectral Image Classification
Multimodal Fusion Transformer for Remote Sensing Image Classification
Unsupervised Deep Learning-Based Pansharpening With Jointly Enhanced Spectral and Spatial Fidelity
UCSL: Towards Unsupervised Common Subspace Learning for Cross-Modal Image Classification
UIU-Net: U-Net in U-Net for Infrared Small Object Detection
A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification

Didn't find your answer? Contact Us

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

Attached Files

#

File Type

File Size

Download

Related Articles

Parsimonious Gaussian Process Models for the Classification of Hyperspectral Remote Sensing Images

Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification

Contact

Like Us On Facebook

Follow Us On Twitter

Links