Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 169-175, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W2/169/2015/
doi:10.5194/isprsarchives-XL-3-W2-169-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
10 Mar 2015
HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS
W. Pervez1, S. A. Khan2, and Valiuddin3 1National University of Sciences and Technology Islamabad, Pakistan
2National University of Sciences and Technology Islamabad, Pakistan
3Hamdard University Karachi, Pakistan
Keywords: Hyperion, hyperspectral, land cover mapping, Imagery Pre-processing, Principle Component Analysis, vegetation delineation Abstract. Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.
Conference paper (PDF, 1181 KB)


Citation: Pervez, W., Khan, S. A., and Valiuddin: HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 169-175, doi:10.5194/isprsarchives-XL-3-W2-169-2015, 2015.

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