Hyperspectral image super-resolution using deep convolutional neural network

[1]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[2]  A. Murat Tekalp,et al.  POCS-based restoration of space-varying blurred images , 1994, IEEE Trans. Image Process..

[3]  Peter M. Atkinson,et al.  Mapping sub-pixel vector boundaries from remotely sensed images , 1996 .

[4]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[5]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[6]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7]  Michael T. Orchard,et al.  New edge directed interpolation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[8]  Peyman Milanfar,et al.  Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement , 2001, IEEE Trans. Image Process..

[9]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[10]  Russell C. Hardie,et al.  Application of the stochastic mixing model to hyperspectral resolution enhancement , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Kinjiro Amano,et al.  Information limits on neural identification of colored surfaces in natural scenes , 2004, Visual Neuroscience.

[12]  Yücel Altunbasak,et al.  Super-resolution reconstruction of hyperspectral images , 2005 .

[13]  Bruno Aiazzi,et al.  Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[14]  H. Shum,et al.  Image super-resolution using gradient profile prior , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  F. Jiru Introduction to post-processing techniques. , 2008, European journal of radiology.

[16]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Chih-Yuan Yang,et al.  Exploiting Self-similarities for Single Frame Super-Resolution , 2010, ACCV.

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

[19]  Stephen Lin,et al.  Super resolution using edge prior and single image detail synthesis , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

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

[22]  Wan-Chi Siu,et al.  Review of image interpolation and super-resolution , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[23]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Hyperspectral Image Segmentation Using S , 2022 .

[24]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for hyperspectral images , 2012, Signal Process..

[25]  Ajmal S. Mian,et al.  Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution , 2014, ECCV.

[26]  Bo Du,et al.  A Discriminative Metric Learning Based Anomaly Detection Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Hui Ming Li Deep Learning for Image Denoising , 2014 .

[28]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[29]  Jocelyn Chanussot,et al.  Hyperspectral image superresolution: An edge-preserving convex formulation , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[30]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[31]  Xiaogang Wang,et al.  DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Aleksandra Pizurica,et al.  Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[34]  Jocelyn Chanussot,et al.  A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[36]  Xing Zhao,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  T. Toda,et al.  Regulation of centriolar satellite integrity and its physiology , 2016, Cellular and Molecular Life Sciences.

[38]  Guangming Shi,et al.  Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation , 2016, IEEE Transactions on Image Processing.

[39]  Lei Shi,et al.  Real-time short-wave infrared hyperspectral conformal imaging sensor for the detection of threat materials , 2016, SPIE Defense + Security.

[40]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Shihong Du,et al.  Learning multiscale and deep representations for classifying remotely sensed imagery , 2016 .

[42]  Shihong Du,et al.  Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Bo Du,et al.  Scene Classification via a Gradient Boosting Random Convolutional Network Framework , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Aggelos K. Katsaggelos,et al.  Super-resolution of compressed videos using convolutional neural networks , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[46]  Antonio J. Plaza,et al.  Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral–Spatial Features , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  Peijun Du,et al.  Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.

[48]  Joan Bruna,et al.  Super-Resolution with Deep Convolutional Sufficient Statistics , 2015, ICLR.

[49]  Carlos Roberto de Souza Filho,et al.  A review on spectral processing methods for geological remote sensing , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[50]  Antonio Moreno,et al.  Breast tumor classification in ultrasound images using texture analysis and super-resolution methods , 2017, Eng. Appl. Artif. Intell..

[51]  Xiangtao Zheng,et al.  Hyperspectral Image Superresolution by Transfer Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Yunsong Li,et al.  Hyperspectral image reconstruction by deep convolutional neural network for classification , 2017, Pattern Recognit..