Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

[1]  Bin Wang,et al.  A Novel Spatial–Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Junjun Jiang,et al.  FusionGAN: A generative adversarial network for infrared and visible image fusion , 2019, Inf. Fusion.

[3]  Shutao Li,et al.  Detection and Correction of Mislabeled Training Samples for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Yi Yu,et al.  Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification , 2017, IEEE Geoscience and Remote Sensing Letters.

[5]  Junjun Jiang,et al.  Locality Preserving Matching , 2018, International Journal of Computer Vision.

[6]  Jelena Kovacevic,et al.  Supervised hyperspectral image classification with rejection , 2016, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  Xiaofei Zhang,et al.  Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

[9]  Lei Guo,et al.  Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Xianming Liu,et al.  Hyperspectral Image Classification in the Presence of Noisy Labels , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Richard J. Murphy,et al.  Evaluating the performance of a new classifier – the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery , 2014 .

[12]  Alan L. Yuille,et al.  Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples , 2016, IEEE Transactions on Image Processing.

[13]  Xiangtao Zheng,et al.  Dimensionality Reduction by Spatial–Spectral Preservation in Selected Bands , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Chen Chen,et al.  Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine , 2014, Remote. Sens..

[15]  Shutao Li,et al.  Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification , 2017, Remote. Sens..

[16]  D. Donoho For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[17]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Dharmendra Singh,et al.  An assessment of independent component analysis for detection of military targets from hyperspectral images , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Mostafa Kaveh,et al.  Fourth-order partial differential equations for noise removal , 2000, IEEE Trans. Image Process..

[20]  Qingquan Li,et al.  Spectral–Spatial Hyperspectral Image Classification Using $\ell_{1/2}$ Regularized Low-Rank Representation and Sparse Representation-Based Graph Cuts , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Rocco A. Servedio,et al.  Boosting in the presence of noise , 2005, J. Comput. Syst. Sci..

[22]  Liangpei Zhang,et al.  Efficient Superpixel-Level Multitask Joint Sparse Representation for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Shuicheng Yan,et al.  Learning With $\ell ^{1}$-Graph for Image Analysis , 2010, IEEE Transactions on Image Processing.

[24]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[25]  Yuan Yuan,et al.  Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation , 2018, Remote. Sens..

[26]  Jian Zhang,et al.  Unsupervised spectral feature selection with l1-norm graph , 2016, Neurocomputing.

[27]  M. Verleysen,et al.  Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Yuan Yan Tang,et al.  Weighted Joint Sparse Representation for Removing Mixed Noise in Image , 2017, IEEE Transactions on Cybernetics.

[29]  Jun Li,et al.  Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Jon Atli Benediktsson,et al.  Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Yuan Yan Tang,et al.  Mixed Noise Removal via Robust Constrained Sparse Representation , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[32]  Lizhe Wang,et al.  SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Xuelong Li,et al.  Discriminant Analysis with Graph Learning for Hyperspectral Image Classification , 2018, Remote. Sens..

[34]  Claire Marais-Sicre,et al.  Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series , 2017, Remote. Sens..

[35]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Lorenzo Bruzzone,et al.  Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances , 2016, IEEE Geoscience and Remote Sensing Magazine.

[37]  Junbin Gao,et al.  Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery , 2018, Remote. Sens..

[38]  Lorenzo Bruzzone,et al.  Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Xuelong Li,et al.  Spectral-Spatial Constraint Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Lin Zhu,et al.  Generative Adversarial Networks for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Jun Huang,et al.  Hyperspectral image denoising with superpixel segmentation and low-rank representation , 2017, Inf. Sci..

[43]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

[44]  Jason Weston,et al.  Semisupervised Neural Networks for Efficient Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Xinge You,et al.  An adaptive hybrid pattern for noise-robust texture analysis , 2015, Pattern Recognit..

[46]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Jun Li,et al.  Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Fei Tony Liu,et al.  Isolation-Based Anomaly Detection , 2012, TKDD.

[49]  Li Ma,et al.  Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised Classification of Remote Sensing Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Dacheng Tao,et al.  Classification with Noisy Labels by Importance Reweighting , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.