Feature reduction of hyperspectral image for classification

Informative feature extraction from hyperspectral image (HSI) is the primary and most challenging task in the hyperspectral data processing. The rich source of HSI information provides effective gr...

[1]  B. Krishna Mohan,et al.  Hyperspectral Image Processing and Analysis , 2015 .

[2]  A. Goshtasby Similarity and Dissimilarity Measures , 2012 .

[3]  Ling Jing,et al.  Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images , 2018 .

[4]  Jacek M. Zurada,et al.  Normalized Mutual Information Feature Selection , 2009, IEEE Transactions on Neural Networks.

[5]  Giovanni Motta Hyperspectral Data Compression , 2006 .

[6]  J. Suykens,et al.  A tutorial on support vector machine-based methods for classification problems in chemometrics. , 2010, Analytica chimica acta.

[7]  Ying Li,et al.  Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network , 2017, Remote. Sens..

[8]  Junwei Han,et al.  Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing , 2014 .

[9]  Fuan Tsai,et al.  Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species , 2007 .

[10]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification With Independent Component Discriminant Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[11]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[12]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[13]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[14]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Qian Du,et al.  An interference rejection approach to noise adjusted principal components transform , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[16]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[17]  John A. Richards,et al.  Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  Guangyi Chen,et al.  Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Saurabh Prasad,et al.  Limitations of Principal Components Analysis for Hyperspectral Target Recognition , 2008, IEEE Geoscience and Remote Sensing Letters.

[20]  Qian Du,et al.  Interference and noise-adjusted principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[21]  Edurne Ibarrola-Ulzurrun,et al.  Assessment of Component Selection Strategies in Hyperspectral Imagery , 2017, Entropy.

[22]  Rob Heylen,et al.  Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[23]  José A. Malpica,et al.  A projection pursuit algorithm for anomaly detection in hyperspectral imagery , 2008, Pattern Recognit..

[24]  John P. Kerekes,et al.  Validation of Abundance Map Reference Data for Spectral Unmixing , 2017, Remote. Sens..

[25]  Pierre Defourny,et al.  Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context , 2018, Remote. Sens..

[26]  Lianru Gao,et al.  Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification , 2017, Remote. Sens..

[27]  Xie Weixin,et al.  Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images , 2015 .

[28]  Mark R. Pickering,et al.  Subspace Detection Using a Mutual Information Measure for Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.

[29]  Guangchun Luo,et al.  Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising , 2016 .

[30]  Jean-Yves Tourneret,et al.  Enhancing Hyperspectral Image Unmixing With Spatial Correlations , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[33]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .