.png){kind=icon}
返回
Semisupervised Band Selection With Graph Optimization for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2020.3037746.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semisupervised band selection (BS) technique plays an important role in processing hyperspectral images (HSIs) because of its superiority of using the limited labeled data and plentiful unlabeled data to select the discriminative and informative feature, which copes with the problem of high-dimensional and scare labeled samples of HSIs. Among semisupervised BS models, graph-based methods are superior to others in many situations and have received more and more attention. However, traditional graph-based models construct a similarity matrix and select valuable bands independently. The similarity matrix remains constant, which will damage the local manifold structure and lead to a suboptimal result. To solve this problem, we propose a semisupervised band selection with an optimal graph (BSOG) approach, which performs BS and local structure learning simultaneously. Instead of fixing the input similarity matrix, the similarity matrix is updated constantly to learn a better local structure. Besides, the learned similarity matrix is adaptive. Then, the optimal band subset can be selected by analyzing the obtained projection matrix An efficient and simple optimization algorithm is proposed to solve this model. Experiments on four real HSIs data validate the effectiveness of the proposed model.
Keyword:
Optimization
Analytical models
Hyperspectral imaging
Feature extraction
Hidden Markov models
Computational modeling
Laplace equations
Band selection (BS)
hyperspectral images (HSIs)
optimal graph
semisupervised
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
.png){kind=icon}
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The changing epidemiology of paediatric brain tumours: a review from the Hospital for Sick Children
PREAI
分享收藏
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis用于高光谱图像分析的局部保持降维与分类
OAAI
OAAI分享收藏A dynamic programming approach to missing data estimation using neural networks使用神经网络进行缺失数据估计的动态规划方法
PREAI
分享收藏
查看更多内容