GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL DATA CLASSIFICATION

Graph based manifold learning and embedding techniques have been very successful at representing high dimensional hyperspectral data in lower dimensions for visualization and classification. Graph based convolutional neural networks (GCNs) have been recently developed for applications on high-dimensional irregular domains represented by graphs, such as citation networks. In this paper, we demonstrate a framework that can leverage GCNs to effectively represent data residing on smooth manifolds, such as reflectance spectra of hyperspectral image pixels. We also propose a robust spatial-spectral semi-supervised adjacency matrix that learns the underlying manifold structure of the data using a limited amount of labeled spectra and a large amount of unlabeled spectra. Classification performance with a benchmark hyperspectral image analysis dataset is also provided that demonstrates the efficacy of this approach.

[1]  Hao Wu,et al.  Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[2]  David W. Messinger,et al.  Spectral-Density-Based Graph Construction Techniques for Hyperspectral Image Analysis , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

[4]  J A Sethian,et al.  Computing geodesic paths on manifolds. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Pierre Vandergheynst,et al.  Wavelets on Graphs via Spectral Graph Theory , 2009, ArXiv.

[6]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[7]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[8]  Richard S. Zemel,et al.  Gated Graph Sequence Neural Networks , 2015, ICLR.

[9]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[10]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[11]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[12]  Y. Zhang,et al.  Spatial context driven manifold learning for hyperspectral image classification , 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Melba M. Crawford,et al.  Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning , 2014, IEEE Signal Processing Magazine.

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[17]  Alán Aspuru-Guzik,et al.  Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.

[18]  Li Ma,et al.  Exploring Nonlinear Manifold Learning for Classification of Hyperspectral Data , 2011 .

[19]  Thomas L. Ainsworth,et al.  Exploiting manifold geometry in hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Joan Bruna,et al.  Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.

[21]  Hao Wu,et al.  Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels , 2018, Pattern Recognit..

[22]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[23]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.