HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

[1]  Arash Mirhashemi,et al.  Introducing spectral moment features in analyzing the SpecTex hyperspectral texture database , 2018, Machine Vision and Applications.

[2]  Michael W. Berry,et al.  Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..

[3]  Torbjørn Skauli,et al.  A collection of hyperspectral images for imaging systems research , 2013, Electronic Imaging.

[4]  Jon Yngve Hardeberg,et al.  Next generation printing - Towards spectral proofing , 2014 .

[5]  Liangpei Zhang,et al.  Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Marius Pedersen,et al.  A sensor-data-based denoising framework for hyperspectral images. , 2015, Optics express.

[7]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[8]  W. Wolfe.  Introduction to Imaging Spectrometers , 1997 .

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Matti Pietikäinen,et al.  Separating color and pattern information for color texture discrimination , 2002, Object recognition supported by user interaction for service robots.

[11]  Kinjiro Amano,et al.  Time-lapse ratios of cone excitations in natural scenes , 2016, Vision Research.

[12]  Antonio Plaza,et al.  A new deep convolutional neural network for fast hyperspectral image classification , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[13]  P. Miller,et al.  Use of tunable liquid crystal filters to link radiometric and photometric standards , 1991 .

[14]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[16]  Kinjiro Amano,et al.  Spatial distributions of local illumination color in natural scenes , 2016, Vision Research.

[17]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[18]  Javier Hernández-Andrés,et al.  Improved Spectral Density Measurement from Estimated Reflectance Data with Kernel Ridge Regression , 2014, ICISP.

[19]  Manoranjan Paul,et al.  Hyperspectral image compression approaches: opportunities, challenges, and future directions: discussion. , 2017, Journal of the Optical Society of America. A, Optics, image science, and vision.

[20]  Lorenzo Bruzzone,et al.  Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Michael S. Brown,et al.  Training-Based Spectral Reconstruction from a Single RGB Image , 2014, ECCV.

[22]  Flávio P. Ferreira,et al.  Statistics of spatial cone-excitation ratios in natural scenes. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Jon Yngve Hardeberg,et al.  A database for spectral image quality , 2015, Electronic Imaging.

[24]  Qin Li,et al.  Hyperspectral unmixing algorithm based on Nonnegative Matrix Factorization , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[25]  Ayan Chakrabarti,et al.  Statistics of real-world hyperspectral images , 2011, CVPR 2011.

[26]  Baohua Zhang,et al.  Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunus persica) , 2015, Comput. Electron. Agric..

[27]  H. Joel Trussell,et al.  Sampling and processing of color signals , 1996, IEEE Trans. Image Process..

[28]  Shree K. Nayar,et al.  Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum , 2010, IEEE Transactions on Image Processing.

[29]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[30]  D. Foster,et al.  Frequency of metamerism in natural scenes. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Edward A. Cloutis,et al.  Near-infrared spectra of ferrous mineral mixtures and methods for their identification in planetary surface spectra , 2014 .

[32]  Bruce J. Tromberg,et al.  Face Recognition in Hyperspectral Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Noël Richard,et al.  A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Erzsébet Merényi,et al.  Classification of hyperspectral imagery with neural networks: comparison to conventional tools , 2014, EURASIP Journal on Advances in Signal Processing.

[35]  Boaz Arad,et al.  Sparse Recovery of Hyperspectral Signal from Natural RGB Images , 2016, ECCV.

[36]  Ary Noviyanto,et al.  Honey dataset standard using hyperspectral imaging for machine learning problems , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[37]  Shutao Li,et al.  PCA-Based Edge-Preserving Features for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Gavin J. Brelstaff,et al.  Hyperspectral camera system: acquisition and analysis , 1995, Remote Sensing.

[39]  Paolo Napoletano,et al.  Combining local binary patterns and local color contrast for texture classification under varying illumination. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[40]  Dacheng Tao,et al.  Discriminative Locality Alignment , 2008, ECCV.

[41]  Pan Xin,et al.  Identification of varieties of natural textile fiber based on Vis/NIR spectroscopy technology , 2015, 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[42]  Jon Y. Hardeberg,et al.  On the spectral dimensionality of object colours , 2002, CGIV.

[43]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[44]  Ludovic Macaire,et al.  Multispectral Demosaicing Using Pseudo-Panchromatic Image , 2017, IEEE Transactions on Computational Imaging.

[45]  Christian Germain,et al.  Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease , 2017, Data in brief.

[46]  Barbara Łydżba-Kopczyńska,et al.  Hyperspectral imaging and multivariate analysis in the dried blood spots investigations , 2018 .

[47]  José Manuel Amigo,et al.  A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging. , 2009, International journal of pharmaceutics.

[48]  Kristalia Melessanaki,et al.  A method for the registration of spectral images of paintings and its evaluation , 2017 .

[49]  Steven D. Hordley,et al.  A multi-spectral image database and its application to image rendering across illumination , 2004, Third International Conference on Image and Graphics (ICIG'04).

[50]  Yong Man Ro,et al.  Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition , 2012, IEEE Transactions on Image Processing.

[51]  Markku Hauta-Kasari,et al.  Spectral image database for observing the quality of Nordic sawn timbers , 2014, Wood Science and Technology.

[52]  Karla Hiller,et al.  MEMS Tunable Fabry-Pérot Filters for Infrared Microspectrometer Applications , 2016 .

[53]  Rune Storvold,et al.  Do it yourself hyperspectral imager for handheld to airborne operations. , 2018, Optics express.

[54]  Sakshi Sharma,et al.  Recent advancement of LBP techniques: A survey , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[55]  Jun-Hu Cheng,et al.  NIR hyperspectral imaging with multivariate analysis for measurement of oil and protein contents in peanut varieties , 2017, Analytical Methods.

[56]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[57]  David Connah,et al.  Multispectral Imaging: How Many Sensors Do We Need? , 2004, Color Imaging Conference.

[58]  Ioanna Kakoulli,et al.  Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications , 2006 .

[59]  Shutao Li,et al.  Hyperspectral image classification based on KNN sparse representation , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[60]  Xiao Xiang Zhu,et al.  Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[61]  R. Younes,et al.  Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas , 2011, 2011 4th International Congress on Image and Signal Processing.

[62]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[63]  Norbert Jung,et al.  Advances in Active Near-Infrared Sensor Systems for Material Classification , 2012 .

[64]  Jussi Parkkinen,et al.  Optimal sampling of color spectra. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[65]  Da-Wen Sun,et al.  Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. , 2017, Meat science.

[66]  Pierre Gouton,et al.  A Database of Spectral Filter Array Images that Combine Visible and NIR , 2017, CCIW.

[67]  Jing Wang,et al.  Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[68]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[69]  Napoleon H. Reyes,et al.  Colour Object Classification Using the Fusion of Visible and Near-Infrared Spectra , 2010, PRICAI.

[70]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[71]  Li Wang,et al.  Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas , 2009 .

[72]  He Yang.  Dimensionality reduction for hyperspectral imagery , 2011 .

[73]  Antonio J. Plaza,et al.  Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[74]  Eva M. Valero,et al.  Outdoor scene reflectance measurements using a Bragg-grating-based hyperspectral imager , 2015 .

[75]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).