A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection

This letter presents a band selection method relying on saliency bands and scale selection (SBSS). The SBSS method is used to excavate the hidden information of hyperspectral images effectively, while its underlying assumptions are: 1) it is reasonable to combine spectral and spatial information to excavate the intrinsic property of a hyperspectral image; 2) there are some saliency bands that can represent a hyperspectral image without significant information loss in data exploitation; and 3) saliency, scale, and image description have an intrinsic connection. The computational complexity of the SBSS method is linear, and experimental results demonstrate that the proposed method obtains competitively good results compared with other state-of-the-art band selection techniques, in terms of classification accuracy.

[1]  Qingquan Li,et al.  A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Qian Du,et al.  A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Qi Wang,et al.  Dual-Clustering-Based Hyperspectral Band Selection by Contextual Analysis , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[5]  Qi Wang,et al.  Hyperspectral Band Selection by Multitask Sparsity Pursuit , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[7]  Adrian J. Brown.  Spectral curve fitting for automatic hyperspectral data analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Timor Kadir,et al.  Scale Saliency and Scene Description , 2002 .

[9]  M. Brady,et al.  Scale Saliency: a novel approach to salient feature and scale selection , 2003 .

[10]  Yongchao Zhao,et al.  A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral Image , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Maoguo Gong,et al.  Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Tony Lindeberg,et al.  Scale Selection , 2020, Computer Vision, A Reference Guide.

[13]  Feifei Xu,et al.  Unsupervised Hyperspectral Band Selection by Dominant Set Extraction , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[15]  A. Agarwal,et al.  Efficient Hierarchical-PCA Dimension Reduction for Hyperspectral Imagery , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[16]  David A. Landgrebe,et al.  Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[18]  Dibyendu Dutta,et al.  Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms , 2015 .

[19]  Fang Liu,et al.  Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[20]  S. Bourennane,et al.  Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction , 2014 .

[21]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[22]  Tony Lindeberg,et al.  Shape from texture from a multi-scale perspective , 1993, 1993 (4th) International Conference on Computer Vision.

[23]  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.

[24]  Paul D. Gader,et al.  Hyperspectral band selection for detecting different blueberry fruit maturity stages , 2014 .

[25]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[27]  David Zhang,et al.  Classification of hyperspectral medical tongue images for tongue diagnosis , 2007, Comput. Medical Imaging Graph..

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

[29]  Bin Luo,et al.  Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[31]  Qian Du,et al.  Low-Rank Subspace Representation for Estimating the Number of Signal Subspaces in Hyperspectral Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[33]  Ashish Ghosh,et al.  Combination of Clustering and Ranking Techniques for Unsupervised Band Selection of Hyperspectral Images , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[35]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .