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 dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) 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 AVIRIS 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, and classiﬁcation accuracies improved by more than 10% on the sample study area, indicating that the proposed methods improve the image quality, thereby aiding in species discrimination.