Study on Alteration Information Extraction by Using Assimilation of Multi-Sensor Spectral Data

Abstract:

Some alterations of similar spectral reflectances cannot be distinguished accurately for their lower spectral resolution when the traditional methods, such as, band ratio and principal component analysis are used to extract alteration information from Landsat ETM multi-spectral data. In this paper, the band1~band7 of MODIS whose wave lengths are among 10~500nm, together with ETM’s multi-spectral bands, whose spatial resolutions are 30m, are chosen in the execution of data assimilation. After the third order wavelet transformation, the low-frequency component of ETM data are replaced by the MODIS data subsequently, then the inverse wavelet transform is in progress. The result of data assimilation consists of not only ETM’s spatial information but also MODIS’ spectral information. At last, four bands of assimilation results are selected to process PCA transform, as a result, two types of alteration in the study area are extracted accurately according to their components.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 943-947
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.943
• Online since: December 2012
• Authors: Ling Jun Zhao, Wan Feng Zhang, Li Fang Zhang, Ji Bo Xie
• Keywords: Data Assimilation, Hyperspectral Imagery Normalization, Spectral Analysis of Mineral Alteration, Wavelet Transform Fusion

References

[1] J. Charney ,M. Halem and R. Jastrow , Use of incomplete historical data to infer the present state of the atmosphere, J Atmos. Sci. 26 (1969) 1160-1163.

DOI: 10.1175/1520-0469(1969)026<1160:uoihdt>2.0.co;2

[2] R. Daley, Atmospheric Data Analysis. New York, USA, (1991).

[3] P. Gong, Image processing for Human Settlements, 3rd Edition,. American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, (2005).

[4] H Bach, W Mauser, Methods and examples for remote sensing data assimilation in land surface process models, IEEE Transactionson Geoscience and Remote Sensing. 41(7), (2003) 1629-1637.

DOI: 10.1109/tgrs.2003.813270

[5] Qi-Guang Miao, Bao-Shu Wang, Multi-sensor image fusion based on improved Laplacian pyramid transform , Acta Optica Sinica. 27(9), (2007) 1605-1610.

[6] M Gonzalez-Audicana, J L Saleta, R G Catalan and R Garcia. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing. 42(6), (2004).

DOI: 10.1109/tgrs.2004.825593

[7] A L CUNHA, J ZHOU and M N DO, Nonsubsampled contourlet transform: filter design and applications in denoising, IEEE International Conference on Image Processing, 9(1), (2005) 49-52.

DOI: 10.1109/icip.2005.1529859

[8] Rong-yuan Chen and Fei-yan Zhang. Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization, Journal of Electronics & Information Technology. 31(10), (2009) 2509-2513.

[9] G R Hunt and R P. Ashley. Spectra of altered rocks in the visible and near infrared, Economic Geology. 74( 1), (1979) 613-1629.

DOI: 10.2113/gsecongeo.74.7.1613