Edge Detection for Hyperspectral Images Using the Bhattacharyya Distance

In this paper, we proposed a new edge detection algorithm for hyper spectral images using the Bhattacharyya distance. First, the principal component analysis is applied to hyper spectral images and then dominant eigenimages are selected. To compute the Bhattacharyya distance, four block pairs of each pixel are extracted: up-down, left-right, diagonal-left down and diagonal-right-down. From each pair of blocks, we compute the Bhattacharyya distance, which was used as edge information. Experiments show promising results compared to the conventional Sobel filter.

[1]  Marco Diani,et al.  An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[2]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[3]  Jon Atli Benediktsson,et al.  Segmentation and classification of hyperspectral images using watershed transformation , 2010, Pattern Recognit..

[4]  Vikash Kumar,et al.  A MRF model-based segmentation approach to classification for multispectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  Jesús Angulo,et al.  Classification of hyperspectral images by tensor modeling and additive morphological decomposition , 2013, Pattern Recognit..

[6]  Jiaxiong Peng,et al.  Double random field models for remote sensing image segmentation , 2004, Pattern Recognit. Lett..

[7]  Wojciech Pieczynski,et al.  SEM algorithm and unsupervised statistical segmentation of satellite images , 1993, IEEE Trans. Geosci. Remote. Sens..

[8]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[9]  Kwanghoon Sohn,et al.  Principal component analysis for compression of hyperspectral images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[10]  Wallace M. Porter,et al.  The airborne visible/infrared imaging spectrometer (AVIRIS) , 1993 .

[11]  David W. Paglieroni,et al.  K-means reclustering: algorithmic options with quantifiable performance comparisons , 2003, SPIE LASE.

[12]  Hamid Hassanpour,et al.  Using Hidden Markov Models for paper currency recognition , 2009, Expert Syst. Appl..