Hyperspectral data clustering based on density analysis ensemble

ABSTRACT In this letter, we present a new hyperspectral data-clustering method, named density analysis ensemble, from a different perspective. Instead of distance-based metrics in traditional clustering methods, we use density analysis for hyperspectral data clustering. Moreover, in order to improve the performance, we use the random subspace ensemble method to formulate a set of clustering systems. The final results are retrieved through majority voting. Compared to the k-means method, the overall accuracies have been improved by 7.05% and 6.93% for the Salinas and Pavia University data sets, respectively.

[1]  Jon Atli Benediktsson,et al.  The spectral-spatial classification of hyperspectral images based on Hidden Markov Random Field and its Expectation-Maximization , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[2]  Gavin Brown,et al.  "Good" and "Bad" Diversity in Majority Vote Ensembles , 2010, MCS.

[3]  Wenzhong Shi,et al.  Unsupervised classification based on fuzzy c-means with uncertainty analysis , 2013 .

[4]  Bor-Chen Kuo,et al.  A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

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

[6]  Menglong Li,et al.  Random Subspace Regression Ensemble for Near-Infrared Spectroscopic Calibration of Tobacco Samples , 2008, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

[7]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[8]  K. R. Remya,et al.  Using weighted majority voting classifier combination for relation classification in biomedical texts , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[9]  Onkar Dikshit,et al.  SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[10]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

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

[12]  M. Güllü,et al.  Phase‐correlation‐based hyperspectral image classification using multiple class representatives obtained with k‐means clustering , 2009 .

[13]  Udo Seiffert,et al.  Cascaded Reduction and Growing of Result Sets for Combining Object Detectors , 2013, MCS.

[14]  Amin Alizadeh Naeini,et al.  Improving the Dynamic Clustering of Hyperspectral Data Based on the Integration of Swarm Optimization and Decision Analysis , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[16]  Udo Seiffert,et al.  Beyond Standard Metrics - On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data , 2014, WSOM.

[17]  Xing Zhao,et al.  Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Huijuan Lu,et al.  A Clustering Algorithm for Datasets with Different Densities , 2009, 2009 International Conference on Computer Technology and Development.

[19]  Jun Li,et al.  An enhanced density peak-based clustering approach for hyperspectral band selection , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[20]  Feng Li,et al.  Large scale hyperspectral data segmentation by random spatial subspace clustering , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.