High Resolution TerraSAR-X Image Speckle Suppression and its Fusion with Multispectral IRS LISS –III Data for Himalayan Glacier Feature Extraction

In this study an attempt is made for studying the Himalayan glacier features using TerraSAR-X and Indian Remote Sensing Satellite, Linear Imaging Self Scanning System III (IRS LISS –III) images. New generation, synthetic aperture radar (SAR) data from TerraSAR-X (TS-X) sensor provide opportunity for glacier feature studies in Himalayan rugged terrain. Spot Light High resolution mode TS-X data give idea about glacial features which remained untraceable from other existing SAR system. However, presence of speckle noise in SAR images degrades the interpretability of the glacier features. Speckle suppression filters (Lee, Frost, Enhanced Lee, Gamma-Map) are applied on SAR intensity images. On the basis of field sight seeing and insitu observations it is observed that still features are not clear. Hence attempt has been made for fusing multitemporal multispatial speckle reduced TS-X SAR data and multispectral IRS LISS-III data for extracting the glacial features such as crevasses, exposed ice and superaglacier lakes. Principal component analysis (PCA) represents the high spectral resolution data in a linear subspace with minimum information loss. Herein, PCA based image fusion technique is adopted for this study and comparison is made between IHS fusion technique and PCA based technique for glacier studies in the Himalayan region.

[1]  Gianluca Bontempi,et al.  New Routes from Minimal Approximation Error to Principal Components , 2008, Neural Processing Letters.

[2]  V. Karathanassi,et al.  A comparison study on fusion methods using evaluation indicators , 2007 .

[3]  D. Yocky Multiresolution wavelet decomposition image merger of landsat thematic mapper and SPOT panchromatic data , 1996 .

[4]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yunhan Dong,et al.  Toward edge sharpening: a SAR speckle filtering algorithm , 2001, IEEE Trans. Geosci. Remote. Sens..

[6]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Konstantinos G. Nikolakopoulos,et al.  Comparative study of fusing ETM data with five different techniques for the broader area of Pyrgos, Greece , 2004, SPIE Remote Sensing.

[8]  George P. Lemeshewsky,et al.  Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges , 2002, SPIE Defense + Commercial Sensing.

[9]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[10]  Xianchuan Yu,et al.  A new algorithm fusing the fractal interpolation and the enhanced lee filter and its application to the SAR image’s denoising , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[11]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

[12]  Jong-Sen Lee,et al.  Refined filtering of image noise using local statistics , 1981 .

[13]  Michael Eineder,et al.  SAR Interferometry with TerraSAR-X , 2003 .

[14]  Jack E. Dibb,et al.  Maximum Temperature Trends in the Himalaya and Its Vicinity: An Analysis Based on Temperature Records from Nepal for the Period 1971-94 , 1999 .

[15]  Gulab Singh,et al.  Spaceborne InSAR Technique for Study of Himalayan Glaciers using ENVISAT ASAR and ERS Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[16]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Y. Zhang,et al.  A new merging method and its spectral and spatial effects , 1999 .

[18]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[19]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Roger L. King,et al.  A wavelet based algorithm for pan sharpening Landsat 7 imagery , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[21]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  D. A. Vaiopoulos,et al.  A comparative study of resolution merge techniques and their efficiency in processing images of urban areas , 2001, IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482).

[23]  George P. Lemeshewsky Multispectral multisensor image fusion using wavelet transforms , 1999 .