期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 27, 期 6, 页码 1279-1289出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2477537
关键词
Band selection; deep learning; hyperspectral image (HSI) classification; manifold ranking (MR); saliency; stacked autoencoders (SAEs)
类别
资金
- National Basic Research Program of China [2013CB336500]
- State Key Program of National Natural Science of China [61232010]
- National Natural Science Foundation of China [61172143, 61105012, 61379094]
- Natural Science Foundation Research Project of Shaanxi Province [2015JM6264]
- Fundamental Research Funds for Central Universities [3102014JC02020G07, 3102015BJ(II)JJZ01]
- Open Research Fund through Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences
Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据