Group Low-Rank Nonnegative Matrix Factorization With Semantic Regularizer for Hyperspectral Unmixing

In this paper, the low rank prior of abundances of hyperspectral data is explored and combined with semantic information to develop a new Group Low-rank constrained Nonnegative Matrix Factorization (GLrNMF) method for linear hyperspectral unmixing. First, hyperspectral image pixels are divided into several groups of superpixels, and then low-rank constraints are cast on them to explore the semantic geometry in both spatial and spectral domains. By incorporating semantic information into the NMF, we can recover more accurate endmembers and abundances in the linear unmixing model. Some experiments are taken on several synthetic and real hyperspectral data to investigate the performance of GLrNMF, and the results show that it can outperform some state-of-the-art unmixing results.

[1]  Qingshan Liu,et al.  Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial–Spectral Feature Fusion , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Xuelong Li,et al.  Manifold Regularized Sparse NMF for Hyperspectral Unmixing , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Antonio J. Plaza,et al.  Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  J. Boardman.  Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .

[5]  Antonio J. Plaza,et al.  A New Preprocessing Technique for Fast Hyperspectral Endmember Extraction , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Hairong Qi,et al.  Low-rank tensor decomposition based anomaly detection for hyperspectral imagery , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[7]  Maurice D. Craig,et al.  Minimum-volume transforms for remotely sensed data , 1994, IEEE Trans. Geosci. Remote. Sens..

[8]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Chiman Kwan,et al.  Bum scar detection using cloudy MODIS images via low-rank and sparsity-based models , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[10]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[11]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[12]  Sen Jia,et al.  Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[13]  José M. Bioucas-Dias,et al.  A variable splitting augmented Lagrangian approach to linear spectral unmixing , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[14]  Zhenyue Zhang,et al.  Low-Rank Matrix Approximation with Manifold Regularization , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Chong-Yung Chi,et al.  A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing , 2009, IEEE Trans. Signal Process..

[16]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[17]  Jun Zhou,et al.  Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Andrei Doncescu,et al.  Feature Selection for Fault Diagnosis Using Fuzzy-ARTMAP Classification and Conflict Intersection , 2010, 2010 International Conference on Broadband, Wireless Computing, Communication and Applications.

[19]  Jing Wang,et al.  Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Shuyuan Yang,et al.  Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Seung-Jean Kim,et al.  Hyperspectral Image Unmixing via Alternating Projected Subgradients , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[22]  Nirmal Keshava,et al.  A Survey of Spectral Unmixing Algorithms , 2003 .

[23]  Xiaojun Wu,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Chiman Kwan,et al.  Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[26]  F. J. García-Haro,et al.  Linear spectral mixture modelling to estimate vegetation amount from optical spectral data , 1996 .

[27]  Patrik O. Hoyer,et al.  Non-negative sparse coding , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[28]  Mario Winter,et al.  N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.

[29]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  V. P. Pauca,et al.  Nonnegative matrix factorization for spectral data analysis , 2006 .

[31]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[32]  Chunxia Zhang,et al.  Enhancing Spectral Unmixing by Local Neighborhood Weights , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Hairong Qi,et al.  Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[34]  José M. Bioucas-Dias,et al.  Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.