.png){kind=icon}
返回
HyperBT: Redundancy Reduction-Based Self-Supervised Learning for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2024.3455234.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Self-supervised learning effectively leverages the information from unlabeled data to extract spatial-spectral features that are both representative and discriminative, partially addressing the challenge of high data annotation costs in hyperspectral image classification. Inspired by the success of redundancy reduction-based self-supervised learning in other domains, we introduce it into HSIC. We proposed a spatial-spectral feature extraction network, HyperBT, to more effectively reduce redundancy. Specifically, we added the off-diagonal terms of the cross-covariance matrix to the loss function and new data augmentation methods, including band bisection and edge weakening. Experimental results demonstrate that our method achieves high accuracy in classification, surpassing many state-of-the-art methods. Through ablation experiments, we validate the effectiveness of each component in the loss function.
Keyword:
HyperBT
hyperspectral image classification (HSIC)
self-supervised learning
spatial-spectral feature
HyperBT
hyperspectral image classification (HSIC)
self-supervised learning
spatial-spectral feature
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
SpectralDiff: A Generative Framework for Hyperspectral Image Classification With Diffusion Models
OAAI
OAAI分享收藏Economic benefit evaluation method for the micro-grid renewable energy system operation微网可再生能源系统运行经济效益评价方法
PREAI
分享收藏Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification基于3D卷积自动编码器的无监督空间光谱特征学习，用于高光谱分类
PREAI
分享收藏ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容