Search Results for author: Zhihua Cai

Found 5 papers, 5 papers with code

Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering

1 code implementation15 Nov 2021 Yaoming Cai, Zijia Zhang, Zhihua Cai, Xiaobo Liu, Yao Ding, Pedram Ghamisi

This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.

Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

1 code implementation15 Nov 2021 Yaoming Cai, Zijia Zhang, Yan Liu, Pedram Ghamisi, Kun Li, Xiaobo Liu, Zhihua Cai

Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool.

Contrastive Learning Online Clustering +1

Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image

1 code implementation22 Apr 2020 Yaoming Cai, Zijia Zhang, Zhihua Cai, Xiaobo Liu, Xinwei Jiang, Qin Yan

In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering.

Graph Embedding

BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image

2 code implementations17 Apr 2019 Yaoming Cai, Xiaobo Liu, Zhihua Cai

The framework consists of a band attention module (BAM), which aims to explicitly model the nonlinear inter-dependencies between spectral bands, and a reconstruction network (RecNet), which is used to restore the original HSI cube from the learned informative bands, resulting in a flexible architecture.

Classification Of Hyperspectral Images Hyperspectral Image Classification

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

1 code implementation26 Jun 2018 Junjun Jiang, Jiayi Ma, Chen Chen, Zhongyuan Wang, Zhihua Cai, Lizhe Wang

(1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.

Dimensionality Reduction General Classification

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