Semi-Supervised FCM and SVM in Co-Training Framework for the Classification of Hyperspectral Images

Collection of labeled samples is very hard, time-taking and costly for the Remote sensing community. Hyperspectral image classification faces various problems due to availability of few numbers of labeled samples. In the recent years, semi-supervised classification methods are used in many ways to solve the problem of labeled samples for the hyperspectral image classification. In this Article, semi supervised fuzzy c-means (FCM) and support vector machine (SVM) are used in co-training framework for the hyperspectral image classification. The proposed technique assumes the spectral bands as first view and extracted spatial features as second view for the co-training process. The experiments have been performed on hyperspectral image data set show that proposed technique is effective than traditional co-training technique.

[1]  Lorenzo Bruzzone,et al.  Active and Semisupervised Learning for the Classification of Remote Sensing Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Filiberto Pla,et al.  Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[3]  Francisco Argüello,et al.  Exploring ELM-based spatial–spectral classification of hyperspectral images , 2014 .

[4]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Friedhelm Schwenker,et al.  Pattern classification and clustering: A review of partially supervised learning approaches , 2014, Pattern Recognit. Lett..

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[8]  Bing Zhang,et al.  A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information , 2014 .

[9]  Nong Sang,et al.  Using clustering analysis to improve semi-supervised classification , 2013, Neurocomputing.

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

[11]  Francisco Herrera,et al.  Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study , 2015, Knowledge and Information Systems.

[12]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[13]  Antonio J. Plaza,et al.  New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jon Atli Benediktsson,et al.  Semisupervised Self-Learning for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Prem Shankar Singh Aydav,et al.  Exploring Self-learning for spatial-spectral classification of remote sensing images , 2015, 2015 International Conference on Computer Communication and Informatics (ICCCI).

[16]  Lorenzo Bruzzone,et al.  Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Jason Weston,et al.  Semisupervised Neural Networks for Efficient Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[19]  Prem Shankar Singh Aydav,et al.  Modified Self-Learning with Clustering for the Classification of Remote Sensing Images , 2015 .

[20]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[21]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[22]  James C. Bezdek,et al.  Partially supervised clustering for image segmentation , 1996, Pattern Recognit..