Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.

[1]  Liu Yi,et al.  Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering , 2015 .

[2]  Hui Zhou,et al.  Multi-branch fusion network for hyperspectral image classification , 2019, Knowl. Based Syst..

[3]  Chih-Sheng Lee,et al.  Multi-objective game-theory models for conflict analysis in reservoir watershed management. , 2012, Chemosphere.

[4]  Bin Zou,et al.  Fusion classification of hyperspectral image based on adaptive subspace decomposition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[5]  Y. Wang,et al.  Band Selection for Hyperspectral Image Classification Based on Improved Particle Swarm Optimization Algorithm , 2014 .

[6]  Maoguo Gong,et al.  Clone Selection Algorithm to Solve Preference Multi-Objective Optimization: Clone Selection Algorithm to Solve Preference Multi-Objective Optimization , 2010 .

[7]  Chen Zhong A Fast Algorithm of Blind Signal Separation Based on ICA , 2004 .

[8]  Lizhong Xu,et al.  A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral , 2013 .

[9]  L. F. Gonzalez,et al.  Hybrid-Game Strategies for multi-objective design optimization in engineering , 2011 .

[10]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[11]  Hui Zhou,et al.  Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification , 2018, ISPRS Int. J. Geo Inf..

[12]  Wang Li-gu Artificial physics optimization algorithm combined band selection for hyperspectral imagery , 2013 .

[13]  Tinglong Dai,et al.  Game Theory and Information Economics , 2018, Handbook of Healthcare Analytics.

[14]  Lizhong Xu,et al.  A Band Selection Method for Hyperspectral Image Classification based on improved Particle Swarm Optimization , 2015 .

[15]  Pierre Defourny,et al.  Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context , 2018, Remote. Sens..

[16]  Wenhua Zeng,et al.  A New Local Search-Based Multiobjective Optimization Algorithm , 2015, IEEE Transactions on Evolutionary Computation.

[17]  Jia Ya-na Dynamic Resource Allocation Algorithm Based on Game Theory in Cognitive Small Cell Networks , 2015 .

[18]  S. Sanjeevi,et al.  Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Ke-ming Yang,et al.  [An algorithm of spectral minimum shannon entropy on extracting endmember of hyperspectral image]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[20]  Wei Chen,et al.  A Band Selection Method for Hyperspectral Image Based on Particle Swarm Optimization Algorithm with Dynamic Sub-Swarms , 2018, J. Signal Process. Syst..

[21]  Yang Dong Clone Selection Algorithm to Solve Preference Multi-Objective Optimization , 2010 .

[22]  Heesung Kwon,et al.  Coalition game theory based feature subset selection for hyperspectral image classification , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[23]  Liming Zhang,et al.  An unsupervised band selection algorithm for hyperspectral imagery based on maximal information: An unsupervised band selection algorithm for hyperspectral imagery based on maximal information , 2012 .

[24]  G. Shaw,et al.  Signal processing for hyperspectral image exploitation , 2002, IEEE Signal Process. Mag..

[25]  Carlos A. Méndez,et al.  Integration of Mathematical Programming and Game Theory for Supply Chain Planning Optimization in Multi-objective competitive scenarios , 2012 .

[26]  P. Gong,et al.  Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping , 2004 .