Results 221 to 230 of about 9,839 (245)
Some of the next articles are maybe not open access.
Unsupervised hyperspectral band selection for apple Marssonina blotch detection
Computers and Electronics in Agriculture, 2018Abstract Apple Marssonina blotch (AMB) is a severe fungal disease that has been plaguing top apple producing countries in the world since it was first found in Japan in 1907. The disease causes premature defoliation and eventually leads to fruit shrinkage and reduction of starch content.
Mubarakat Shuaibu +5 more
openaire +1 more source
A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization
IEEE Geoscience and Remote Sensing Letters, 2017Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate.
Xia Xu, Zhenwei Shi, Bin Pan
exaly +2 more sources
Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging
2007Different 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 +3 more
openaire +1 more source
Unsupervised Hyperspectral Band Selection Based on Spectral Rhythm Analysis
2014 27th SIBGRAPI Conference on Graphics, Patterns and Images, 2014Remote 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 Chaves Brandao dos Santos +3 more
openaire +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015 Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure.
Susmita Ghosh +2 more
exaly +2 more sources
Ant colony optimization for supervised and unsupervised hyperspectral band selection
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013In 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 +5 more
openaire +1 more source
Comparison of traditional and recent unsupervised band selection approaches in hyperspectral images
2016 24th Signal Processing and Communication Application Conference (SIU), 2016In this paper, well-known traditional band selection methods which are used in hyperspectral imaging, namely, Maximum-Variance Principal Component Analysis (MVPCA), Maximum-SNR Principal Component Analysis (MSNRPCA), k-means, k-medoids, and recently proposed Automatic Band Selection (ABS) and Band Column Selection (BCS) approaches are compared.
Ali Can Karaca, Mehmet Kemal Güllü
openaire +3 more sources
A novel unsupervised bands selection algorithm for hyperspectral image
Optik, 2018Abstract 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 +3 more
openaire +1 more source
Determining the dimensionality of hyperspectral imagery for unsupervised band selection
SPIE Proceedings, 2003This 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
Unsupervised Hyperspectral Band Selection by Fuzzy Clustering With Particle Swarm Optimization
IEEE Geoscience and Remote Sensing Letters, 2017Due to the lack of label information and the intrinsic complexity of hyperspectral images (HSIs), unsupervised band selection is always one of the most challenging tasks in HSI processing. Fuzzy clustering is a promising technique for unsupervised band selection, which can partition unlabeled data into groups effectively.
Maoguo Gong, Mingyang Zhang
exaly +2 more sources