- An Adaptive Noise Filtering Algorithm for AVIRIS Data with Implications for Classiﬁcation AccuracyPhillips, Rhonda D.; Blinn, Christine E.; Watson, Layne T.; Wynne, Randolph H. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2008)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.
- Continuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 2Phillips, Rhonda D.; Watson, Layne T.; Wynne, Randolph H.; Ramakrishnan, Naren (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009)This paper describes in detail the continuous iterative guided spectral class rejection (CIGSCR) classification method based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. In CIGSCR and IGSCR, training data are used to evaluate the strength of the association between a particular cluster and a class, and a statistical hypothesis test is used to determine which clusters should be associated with a class and used for classification and which clusters should be rejected and possibly reﬁned. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that inﬂuence iterations. Furthermore, evidence is presented that the semisupervised clustering in CIGSCR produces more accurate classifications than classification based on clustering without supervision.
- Feature Reduction using a Singular Value Decomposition for the Iterative Guided Spectral Class Rejection Hybrid ClassifierPhillips, Rhonda D.; Watson, Layne T.; Wynne, Randolph H.; Blinn, Christine E. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2007)Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This work introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/nonforest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVDbased feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondonia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVDbased feature reduction can produce statistically significantly better classifications than PCA.