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An Adaptive Noise-Filtering Algorithm for AVIRIS Data With Implications for Classification Accuracy

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4 Author(s)
Phillips, R.D. ; Dept. of Comput. Sci., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA ; Blinn, C.E. ; Watson, L.T. ; Wynne, R.H.

This paper describes a new algorithm used to adaptively filter a remote-sensing data set based on signal-to-noise ratios (SNRs) once the maximum noise fraction has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into ldquobinsrdquo with other bands having similar SNRs. A median filter with a variable-sized kernel is then applied to each band, with the same size kernel used for each band in a particular bin. The proposed adaptive filters are applied to a hyperspectral image generated by the airborne visible/infrared imaging spectrometer sensor, and results are given for the identification of three different pine species located within the study area. The adaptive-filtering scheme improves image quality as shown by estimated SNRs. Classification accuracies of three pine species improved by more than 10% in the study area as compared to that achieved by the same discriminant method without adaptive spatial filtering.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:47 ,  Issue: 9 )