Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques

In this paper, we propose a novel adaptive band selection approach for hyperspectral image semantic interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image semantic interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the semantic interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy.

[1]  Qian Du,et al.  Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Zhen Ji,et al.  Band Selection for Hyperspectral Imagery Using Affinity Propagation , 2008, 2008 Digital Image Computing: Techniques and Applications.

[4]  James E. Fowler,et al.  Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Jun Zhou,et al.  Semisupervised Hyperspectral Band Selection Via Spectral–Spatial Hypergraph Model , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[7]  C. A. Murthy,et al.  Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Kang Sun,et al.  A New Band Selection Method for Hyperspectral Image Based on Data Quality , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Yicong Zhou,et al.  Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Jun Zhou,et al.  Band Weighting via Maximizing Interclass Distance for Hyperspectral Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[11]  Hong Huang,et al.  Improved discriminant sparsity neighborhood preserving embedding for hyperspectral image classification , 2014, Neurocomputing.

[12]  Ludmila I. Kuncheva,et al.  A stability index for feature selection , 2007, Artificial Intelligence and Applications.

[13]  Qian Du,et al.  Hyperspectral Image Classification Using Band Selection and Morphological Profiles , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Giles M. Foody,et al.  Feature Selection for Classification of Hyperspectral Data by SVM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[15]  I. Farah,et al.  High-level hyperspectral image classification based on spectro-spatial dimensionality reduction , 2016 .

[16]  Wesley E. Snyder,et al.  Band selection using independent component analysis for hyperspectral image processing , 2003, 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings..

[17]  Licheng Jiao,et al.  Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Qian Du,et al.  Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis , 2008, IEEE Geoscience and Remote Sensing Letters.

[19]  Lei Yu,et al.  Stable and Accurate Feature Selection , 2009, ECML/PKDD.

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

[21]  Chunhui Zhao,et al.  Feature weighting algorithms for classification of hyperspectral images using a support vector machine. , 2014, Applied optics.

[22]  Chein-I Chang,et al.  Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Licheng Jiao,et al.  Dimensionality Reduction Based on Group-Based Tensor Model for Hyperspectral Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[24]  Jon Atli Benediktsson,et al.  Random-Walker-Based Collaborative Learning for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Isabel Praça,et al.  Support Vector Machines for decision support in electricity markets' strategic bidding , 2016, Neurocomputing.

[26]  Nicolas Courty,et al.  Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[27]  P. Deepa,et al.  Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[28]  Tao Lin,et al.  Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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

[30]  Qiang Ye,et al.  A Novel Method for Hyperspectral Image Classification Based on Laplacian Eigenmap Pixels Distribution-Flow , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Bor-Chen Kuo,et al.  Feature Mining for Hyperspectral Image Classification , 2013, Proceedings of the IEEE.

[32]  Joachim Diederich,et al.  Rule Extraction from Support Vector Machines , 2008, Studies in Computational Intelligence.

[33]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[34]  R. Real,et al.  The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .

[35]  Maryam Imani,et al.  Feature Extraction Using Weighted Training Samples , 2015, IEEE Geoscience and Remote Sensing Letters.

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

[37]  Jesús Angulo,et al.  Classification of hyperspectral images by tensor modeling and additive morphological decomposition , 2013, Pattern Recognit..

[38]  Bernhard Schölkopf,et al.  Remote Sensing Feature Selection by Kernel Dependence Measures , 2010, IEEE Geoscience and Remote Sensing Letters.

[39]  Salah Bourennane,et al.  Dimensionality Reduction Based on Tensor Modeling for Classification Methods , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Chein-I Chang,et al.  Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Adolfo Martínez Usó,et al.  Clustering-Based Hyperspectral Band Selection Using Information Measures , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Jiasong Zhu,et al.  Discriminative Gabor Feature Selection for Hyperspectral Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[43]  Filiberto Pla,et al.  Cluster validation using information stability measures , 2010, Pattern Recognit. Lett..

[44]  J.A. Benediktsson,et al.  Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.