Results 151 to 160 of about 53,858 (186)

A novel unsupervised bands selection algorithm for hyperspectral image

Optik, 2018
Abstract A novel bands selection method based on ABS (Adaptive Band Selection) and JSKF (Joint Skewness-Kurtosis Figure) is proposed in this paper. The hyperspectral data is separated into different sub-spaces by employing ABS and JSKF respectively. Subsequently a novel optimal bands selection method NIA (Normalization Index Algorithm) is proposed to
Xiaoping Du, Hang Chen, Zhengjun Liu, Chengwei Yang   +3 more
openaire   +1 more source

Unsupervised Hyperspectral Band Selection Based on Spectral Rhythm Analysis

2014 27th SIBGRAPI Conference on Graphics, Patterns and Images, 2014
Remote sensing image classification aims to automatically categorize a monitored area in land cover classes. Hyperspectral images, which provide plenty of spectral information per pixel, allow achieving good accuracy results in classification problems.
Lilian C. B. dos Santos, Silvio Jamil Ferzoli Guimaraes, Arnaldo de Albuquerque Araujo, Jefersson A. dos Santos   +3 more
openaire   +1 more source

Unsupervised Band Selection Using Block-Diagonal Sparsity for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2017
In order to alleviate the negative effect of curse of dimensionality, band selection is a crucial step for hyperspectral image (HSI) processing. In this letter, we propose a novel unsupervised band selection approach to reduce the dimensionality for hyperspectral imagery. In order to obtain the most representative bands, the correlation matrix computed
Wang, J., Zhang, K., Pei, P., Madani, K., Sabourin, C.   +4 more
openaire   +2 more sources

Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging

2007
Different methods have been proposed in order to deal with the huge amount of information that hyperspectral applications involve. This paper presents a comparison of some of the methods proposed for band selection. A relevant and recent set of methods have been selected that cover the main tendencies in this field.
Adolfo Martínez-Usó, Filiberto Pla, Jose M. Sotoca, Pedro García-Sevilla   +3 more
openaire   +1 more source

An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection

Proceedings of International Conference on Internet Multimedia Computing and Service, 2014
In remote sensing data processing, band selection is very important for hyperspectral image processing and analysis, which utilize the most distinctive and informative band subset of original bands to reduce data dimensionality. Although band selection can significantly alleviate the computational burden, the process itself may cause additional ...
Yan Cao, Jing Zhang, Li Zhuo, Chao Wang, Qianlan Zhou   +4 more
openaire   +1 more source

Determining the dimensionality of hyperspectral imagery for unsupervised band selection

SPIE Proceedings, 2003
This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a Hyperspectral Image. Algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images ...
Alejandra Umana-Diaz, Miguel Velez-Reyes
openaire   +1 more source

Ant colony optimization for supervised and unsupervised hyperspectral band selection

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost.
Jianwei Gao, Qian Du, Lianru Gao, Xu Sun, Yuanfeng Wu, Bing Zhang   +5 more
openaire   +1 more source

Unsupervised Band Selection Based on Group-Based Sparse Representation

2017
Band selection (BS) is one of the important topics in hyperspectral image data analysis. How to search the representative bands that can effectively represent the image with lower inter-band redundancy is an long-term issue. Recently, the sparse representation (SR) was used to solve BS problem, called SR-BS.
Hung-Chang Chien, Chih-Hung Lai, Keng-Hao Liu   +2 more
openaire   +1 more source

Boltzmann Entropy-Based Unsupervised Band Selection for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2019
Band selection for hyperspectral images helps improve the efficiency of data processing and even the accuracy of classification. It is to reduce the dimensionality of a hyperspectral image by selecting representative bands. In such a process, the quantification of band similarity is the fundamental issue, and it is usually achieved by using an ...
Peichao Gao, Jicheng Wang, Hong Zhang, Zhilin Li   +3 more
openaire   +1 more source

Unsupervised Hyperspectral Band Selection Method Based on Low-Rank Representation

2019
In order to reduce the spectral redundancy of hyperspectral remote sensing images and reduce the computational complexity of subsequent processing, an unsupervised hyperspectral image band selection algorithm based on low-rank representation (LRBS) was proposed in this paper. First, a low-rank representation of the hyperspectral image is proposed and a
Chunyan Yu, Kun Cen, Chein-I Chang, Fang Li   +3 more
openaire   +1 more source