Results 221 to 230 of about 23,100 (259)

Hyperspectral band selection using statistical models

SPIE Proceedings, 2011
ABSTRACT Hyperspectral sensors are delivering a data cube consisting of hundreds of images gathered in adjacent frequencybands. Processing such data requires solutions to handle the computational complexity and the informationredundancy. In principle, there are two dierent approaches deployable.
Jochen Maerker, Wolfgang Groß, Wolfgang Middelmann, Alfons Ebert   +3 more
openaire   +1 more source

Constrained band selection for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2006
Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (
null Chein-I Chang, null Su Wang
openaire   +1 more source

Hyperspectral Band Selection Based on Endmember Dissimilarity for Hyperspectral Unmixing

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Hyperspectral remote sensing could acquire hundreds of bands to cover a complete spectral interval, which deliver more information and allow a whole range of new and more precise applications. But vast data volume can cause trouble in computer processing and data transmission.
Mingming Xu, Yuxiang Zhang, Jie Li, Jiayi Li, Dongmei Song, Yanguo Fan, Ning Sun   +6 more
openaire   +1 more source

Unsupervised Hyperspectral Image Band Selection via Column Subset Selection

IEEE Geoscience and Remote Sensing Letters, 2015
In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns.
null Chi Wang, null Maoguo Gong, null Mingyang Zhang, null Yongqiang Chan   +3 more
openaire   +1 more source

Unsupervised band selection for hyperspectral image analysis

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that using a subset of original bands the accuracy is unchanged or tolerably degraded ...
null Qian Du, null He Yang
openaire   +1 more source

Hyperspectral Band Selection via Optimal Neighborhood Reconstruction

IEEE Transactions on Geoscience and Remote Sensing, 2020
Band selection is one of the most important technique in the reduction of hyperspectral image (HSI). Different from traditional feature selection problem, an important characteristic of it is that there is usually strong correlation between neighboring bands, that is, bands with close indexes.
Qi Wang, Fahong Zhang, Xuelong Li
openaire   +1 more source

Unsupervised Hyperspectral Band Selection by Sequential Clustering

Proceedings of the International Conference on Watermarking and Image Processing, 2017
Hyperspectral data provide detailed information about the spectral properties of an observed scene. Although hyperspectral images contain much information, the reduction of dimensionality of these data is sometimes necessary to minimize their processing complexity. Band selection techniques are ways to perform dimensionality reduction. These techniques
Mohammed Bilel Amri, Sarra Ikram Benabadji, Moussa Sofiane Karoui   +2 more
openaire   +1 more source

Linearly constrained band selection for hyperspectral imagery

SPIE Proceedings, 2006
Linearly constrained adaptive beamforming has been used to design hyperspectral target detection algorithms such as constrained energy minimization (CEM) and linearly constrained minimum variance (LCMV). It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures.
Su Wang, Chein-I Chang
openaire   +1 more source

Hyperspectral band selection based on graph clustering

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012
In this paper we present a new method for hyperspectral band selection problem. The principle is to create a band adjacency graph (BAG) where the nodes represent the bands and the edges represent the similarity weights between the bands. The Markov Clustering Process (abbreviated MCL process) defines a sequence of stochastic matrices by alternation of ...
Rachid Hedjam, Mohamed Cheriet
openaire   +1 more source

HYBASE - HYperspectral BAnd SElection tool

2008
Band selection is essential in the design of multispectral sensor systems. This paper describes the TNO hyperspectral band selection tool HYBASE. It calculates the optimum band positions given the number of bands and the width of the spectral bands. HYBASE is used to calculate the minimum number of spectral bands that is required to get the best target
Schwering, P.B.W., Bekman, H.H.P.T., Seijen, H.H. van   +2 more
openaire   +1 more source