Hyperspectral band selection based on a variable precision neighborhood rough set.

Band selection is a well-known approach for reducing dimensionality in hyperspectral images. We propose a band-selection method based on the variable precision neighborhood rough set theory to select informative bands from hyperspectral images. A decision-making information system was established by hyperspectral data derived from soybean samples between 400 and 1000 nm wavelengths. The dependency was used to evaluate band significance. The optimal band subset was selected by a forward greedy search algorithm. After adjusting appropriate threshold values, stable optimized results were obtained. To assess the effectiveness of the proposed band-selection technique, two classification models were constructed. The experimental results showed that admitting inclusion errors could improve classification performance, including band selection and generalization ability.

[1]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[2]  Jie Gui,et al.  Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction , 2010, Comput. Biol. Medicine.

[3]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

[4]  Manuel Graña,et al.  Face recognition with lattice independent component analysis and extreme learning machines , 2012, Soft Comput..

[5]  Ashish Ghosh,et al.  Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination , 2014 .

[6]  Maryam Imani,et al.  Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples , 2014, IEEE Geoscience and Remote Sensing Letters.

[7]  Qinghua Hu,et al.  Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation , 2007, Pattern Recognit..

[8]  Qinghua Hu,et al.  Neighborhood rough set based heterogeneous feature subset selection , 2008, Inf. Sci..

[9]  Saroj K. Meher.  Knowledge-Encoded Granular Neural Networks for Hyperspectral Remote Sensing Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Jiye Liang,et al.  International Journal of Approximate Reasoning Multigranulation Decision-theoretic Rough Sets , 2022 .

[11]  Abdulmotaleb El-Saddik,et al.  Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks , 2014, Sensors.

[12]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[13]  David Casasent,et al.  Feature reduction and morphological processing for hyperspectral image data. , 2004, Applied optics.

[14]  Jon Atli Benediktsson,et al.  Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques , 2012 .

[15]  Qian Du,et al.  Optimized Hyperspectral Band Selection Using Particle Swarm Optimization , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[17]  Nan Liu,et al.  Voting based extreme learning machine , 2012, Inf. Sci..

[18]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

[19]  Zhe Liu,et al.  Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. , 2014, Applied optics.

[20]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[21]  Yee Leung,et al.  A rough set approach to the discovery of classification rules in spatial data , 2007, Int. J. Geogr. Inf. Sci..

[22]  Xin Pan,et al.  A variable precision rough set approach to the remote sensing land use/cover classification , 2010, Comput. Geosci..

[23]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[24]  Adolfo Martínez Usó,et al.  Clustering-Based Hyperspectral Band Selection Using Information Measures , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[26]  Qinghua Hu,et al.  Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine , 2012 .

[27]  Jianbin Qiu,et al.  A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search , 2014, Eng. Appl. Artif. Intell..

[28]  Qinghua Hu,et al.  Mixed feature selection based on granulation and approximation , 2008, Knowl. Based Syst..

[29]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[30]  Cheng-Lung Huang,et al.  A distributed PSO-SVM hybrid system with feature selection and parameter optimization , 2008, Appl. Soft Comput..

[31]  Qian Du,et al.  An Efficient Method for Supervised Hyperspectral Band Selection , 2011, IEEE Geoscience and Remote Sensing Letters.

[32]  Nick Cercone,et al.  Discovering rules for water demand prediction: An enhanced rough-set approach☆ , 1996 .

[33]  Peijun Du,et al.  Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection. , 2012, Applied optics.

[34]  Yi Lin,et al.  Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets , 2011 .

[35]  Yu Peng,et al.  DDEUDSC: A Dynamic Distance Estimation using Uncertain Data Stream Clustering in mobile wireless sensor networks , 2014 .

[36]  Lorenzo Bruzzone,et al.  A Feature-Metric-Based Affinity Propagation Technique for Feature Selection in Hyperspectral Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[37]  T. Næs,et al.  The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy , 1988 .

[38]  Qingxin Zhu,et al.  Quantitative analysis for covering-based rough sets through the upper approximation number , 2013, Inf. Sci..