Mr. Chinmaya Panda Profile

Chinmaya Panda

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Publications (1)

Proceedings Article | 30 April 2016 Paper

Evaluation and effect of various noise reduction techniques used before atmospheric correction of Hyperion data

Chinmaya Panda, Vinay Kumar, Kamal Pandey, G. Jyothi

Proceedings Volume 9880, 98800Z (2016) https://doi.org/10.1117/12.2224047

KEYWORDS: Atmospheric corrections, Principal component analysis, Independent component analysis, Sensors, Error analysis, Reflectivity, Denoising, Remote sensing, Amplifiers, Vegetation

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Remote sensing, in recent days, has seen the emergence of hyperspectral sensors as the helping aspect to numerous applications. Hyperspectral remote sensing often contains data with narrow spectral bandwidth (10nm) that enables the feature identification and distinction of spectral similar features. Hyperion L1R data consists of 242 bands and out of which some bands contain noise (a pattern reorganization that hinders information). After removal of these sensor errors like bad bands and dropped line error, atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) is carried out to get true ground reflectance. It was observed in the spectral reflectance curve of the obtained product is still affected with several noise and in order to remove this noise some corrections are required before atmospheric correction. In this study various noise reduction techniques like Minimum Noise Fraction (MNF), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are implemented to observe their effect on sensor error removed Hyperion data before carrying out atmospheric correction. At first noise reduction techniques (MNF, PCA and ICA) are applied separately to the Hyperion sensor error corrected product and then inverse of (MNF, PCA and ICA) are carried out respectively. It was observed that applying atmospheric correction after applying MNF & inverse MNF was giving better spectral profiles of the features (i.e. with less noise). From the above spectral profiles we can conclude that the data which is atmospherically corrected after applying MNF & inverse MNF is giving better results i.e. the spectral profiles of vegetation, water, urban and coal field is smooth and less noisy in comparison with the spectral profiles obtained by directly applying atmospheric correction and the spectral profiles obtained by applying PCA & inverse PCA and ICA &inverse ICA. After applying PCA & inverse PCA and then atmospheric correction the spectral profiles of the objects are smooth but not as good as those spectral profile obtained by applying MNF & inverse MNF. It was also observed that the nature of reflectance of the feature at the Short Wave Infrared (SWIR) region has abruptly increased after applying ICA & inverse ICA and atmospheric correction. So the atmospherically corrected data obtained after applying ICA & inverse ICA cannot be used for further processing. The atmospherically corrected data obtained after applying MNF & inverse MNF can be used for further hyperspectral data processing like feature identification and classification as it gives the true spectral nature of the features.

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