Multiple composite kernel learning for hyperspectral image classification

In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral and spatial information (e.g., extinction profiles). Then, the new spectral and spatial features are integrated into the composite kernels based on support vector machines classifier. Different rotation matrices will lead to obtaining various newly spectral and spatial characteristics, thereby they further increase the diversity and the classification performance. Experimental results on Indian Pines benchmark hyperspectral dataset demonstrate the excellent performance of the proposed method.

[1]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[3]  Peijun Du,et al.  Random Subspace Ensembles for Hyperspectral Image Classification With Extended Morphological Attribute Profiles , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Jon Atli Benediktsson,et al.  Extinction Profiles for the Classification of Remote Sensing Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Michel Grimaud,et al.  New measure of contrast: the dynamics , 1992, Optics & Photonics.

[7]  Peijun Du,et al.  Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Chein-I Chang,et al.  Hyperspectral Data Exploitation , 2007 .

[9]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Jon Atli Benediktsson,et al.  Hyperspectral Data Classification Using Extended Extinction Profiles , 2016, IEEE Geoscience and Remote Sensing Letters.

[12]  Wei Zhang,et al.  Multiple Classifier System for Remote Sensing Image Classification: A Review , 2012, Sensors.

[13]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

[14]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[15]  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.

[16]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[17]  Peijun Du,et al.  Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.