Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.

[1]  Wenzhong Guo,et al.  Land-Use Classification via Extreme Learning Classifier Based on Deep Convolutional Features , 2017, IEEE Geoscience and Remote Sensing Letters.

[2]  Tie Qiu,et al.  Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder , 2017, IEEE Access.

[3]  Haokui Zhang,et al.  Deep learning for remote sensing image classification: A survey , 2018, WIREs Data Mining Knowl. Discov..

[4]  Jon Atli Benediktsson,et al.  Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data , 2009, IEEE Geoscience and Remote Sensing Letters.

[5]  Jun Zhou,et al.  Region-Based Structure Preserving Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Luis Eduardo Mujica,et al.  Q-statistic and T2-statistic PCA-based measures for damage assessment in structures , 2011 .

[7]  Wei Feng,et al.  Weight-Based Rotation Forest for Hyperspectral Image Classification , 2017, IEEE Geoscience and Remote Sensing Letters.

[8]  Jayashree D. Mallapur,et al.  Remote sensing and controlling of greenhouse agriculture parameters based on IoT , 2017, 2017 International Conference on Big Data, IoT and Data Science (BID).

[9]  Naif Alajlan,et al.  Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images , 2014, IEEE Geoscience and Remote Sensing Letters.

[10]  Peijun Du,et al.  Hyperspectral Remote Sensing Image Classification Based on Rotation Forest , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  Nicolás García-Pedrajas,et al.  Supervised projection approach for boosting classifiers , 2009, Pattern Recognit..

[12]  Shuyuan Yang,et al.  Group Low-Rank Nonnegative Matrix Factorization With Semantic Regularizer for Hyperspectral Unmixing , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Claus Boyens,et al.  Handbook of Computational Statistics , 2005 .

[14]  Mohammad Zaheer Ansari,et al.  Imaging functional blood vessels by the laser speckle imaging (LSI) technique using Q-statistics of the generalized differences algorithm. , 2016, Microvascular research.

[15]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[16]  Héctor Rabal,et al.  Q-statistics in dynamic speckle pattern analysis , 2012 .

[17]  Rui Zhang,et al.  Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy , 2018, Sensors.

[18]  Mahesh Pal.  Extreme‐learning‐machine‐based land cover classification , 2008, ArXiv.

[19]  Christos G. Tsinos,et al.  Distributed Blind Hyperspectral Unmixing via Joint Sparsity and Low-Rank Constrained Non-Negative Matrix Factorization , 2017, IEEE Transactions on Computational Imaging.

[20]  Xiaoyu Chen,et al.  A review on image classification of remote sensing using deep learning , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[21]  Minqiang Li,et al.  Coevolutionary learning of neural network ensemble for complex classification tasks , 2012, Pattern Recognit..

[22]  Naoto Yokoya,et al.  Ensemble of transfer component analysis for domain adaptation in hyperspectral remote sensing image classification , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[23]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[24]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Antanas Verikas,et al.  Random forests based monitoring of human larynx using questionnaire data , 2012, Expert Syst. Appl..

[26]  Manuel Graña,et al.  Spatially regularized semisupervised Ensembles of Extreme Learning Machines for hyperspectral image segmentation , 2015, Neurocomputing.

[27]  Yannick Deville,et al.  Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Gui-Song Xia,et al.  Nonnegative discriminative manifold learning for hyperspectral data dimension reduction , 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[29]  Timothy A. Warner,et al.  Kernel-based extreme learning machine for remote-sensing image classification , 2013 .

[30]  Ulrich Schmid,et al.  A novel MEMS-based piezoelectric multi-modal vibration energy harvester concept to power autonomous remote sensing nodes for Internet of Things (IoT) applications , 2015, 2015 IEEE SENSORS.

[31]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Qian Du,et al.  Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density , 2018, IEEE Geoscience and Remote Sensing Letters.

[33]  Tie Qiu,et al.  EABS: An Event-Aware Backpressure Scheduling Scheme for Emergency Internet of Things , 2018, IEEE Transactions on Mobile Computing.

[34]  Wei Liu,et al.  Multiple features fusion for hyperspectral image classification based on extreme learning machine , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[35]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  C. L. Philip Chen,et al.  A Data-Emergency-Aware Scheduling Scheme for Internet of Things in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.

[37]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[38]  Keqiu Li,et al.  How Can Heterogeneous Internet of Things Build Our Future: A Survey , 2018, IEEE Communications Surveys & Tutorials.