Fusion of hyperspectral and lidar data using generalized composite kernels: A case study in Extremadura, Spain

The light detection and ranging (LiDAR) data provides very valuable information about the height of the surveyed area which can be used as a source of complementary information for the classification of hyperspectral data, in particular when it is difficult to separate complex classes. In this work, we suggest to exploit the generalized composite kernel strategy for fusion and classification of hyperspectral and LiDAR data. Our experimental results, conducted using a hyperspectral image and a LiDAR derived intensity image collected over a rural area in Extremadura, Spain, indicate that the proposed framework for the fusion of hyperspactral and LiDAR data provides significant classification results.

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

[2]  Jocelyn Chanussot,et al.  Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Craig L. Glennie,et al.  Voxelization of full waveform LiDAR data for fusion with Hyperspectral Imagery , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[4]  Nicolas H. Younan,et al.  DTM extraction of lidar returns via adaptive processing , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  Lorenzo Bruzzone,et al.  Extended profiles with morphological attribute filters for the analysis of hyperspectral data , 2010 .

[6]  A. Pižurica,et al.  GRAPH-BASED FEATURE FUSION OF HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA USING MORPHOLOGICAL FEATURES , 2013 .

[7]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[9]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[10]  Saurabh Prasad,et al.  Overcoming the Small Sample Size Problem in Hyperspectral Classification and Detection Tasks , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

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

[13]  Edoardo Pasolli,et al.  A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Jon Atli Benediktsson,et al.  Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles , 2012, IEEE Journal of Selected Topics in Signal Processing.