High-dimensional image data open new possibilities in remote sensing digital image classification, particularly when dealing with classes that are spectrally very similar. The main problem refers to the estimation of a large number of classifier's parameters. One possible solution to this problem consists in reducing the dimensionality of the original data without a significant loss of information. In this letter, a new approach to reduce data dimensionality is proposed. In the proposed methodology, each pixel's curve of spectral response is initially segmented, and the digital numbers (DNs) at each segment are replaced by a smaller number of statistics. In this letter, the proposed statistics are the mean and variance of the segment's DNs, which are supposed to carry information about the segment's position and shape, respectively. Tests were performed by using Airborne Visible/Infrared Imaging Spectrometer hyperspectral image data. The experiments have shown that this methodology is capable of providing very acceptable results, in addition of being computationally efficient

Logistic discrimination can be regarded as a partially parametric approach to pattern recognition. The method is quite general and robust: it assumes nothing about the probability distribution of variables and requires the estimation of fewer parameters than some better-known procedures such as the Gaussian maximum likelihood discriminator. This paper describes the logistic discrimination model and gives results obtained when using it to classify an AVIRIS image with classes that are spectrally very similar.

Binary tree-structured rules can be viewed in terms of repeated splits of subsets of the feature space into two descendant subsets, starting from the entire feature space and ending in a partition of the feature space associated with each class. This paper presents a brief introduction to binary decision trees and shows results obtained in the classifying Landsat-TM and AVIRIS digital images.

This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained. Joint use of space and scale consistency is applied for better preservation of edges. The shrinkage functions are combined to preserve edges that appear simultaneously at several resolutions, and geometric constraints are applied to preserve edges that are not isolated. The proposed technique produces a filtered version of the original image, where homogeneous regions appear separated by well-defined edges. Possible applications include image presegmentation, and image denoising.