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

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 "bins" 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.