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14 April 2000 Multiresolution feature extraction for pairwise classification of hyperspectral data
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Prediction of landcover type from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in more than 100 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. The bands are ordered by their wavelengths and spectrally adjacent bands are generally statistically correlated. Using such high dimensional data for classification of landcover potentially provides greatly improved results. However, it is necessary to select bands that provide the best possible discrimination among the classes of interest. In this paper, we propose an efficient top-down multiresolution class-dependent feature extraction algorithm for hyperspectral data to be used with a pairwise classification scheme. First, the C class problem is divided into (C2) two class problems. Features for each pair of classes are extracted independently. The algorithm decomposes the bands recursively into groups of adjacent bands (subspaces) in a top-down fashion. The features extracted are specific to the pair of classes that are being distinguished and exploit the ordering information in the hyperspectral data. Experiments on a 183 band AVIRIS data set for a 12 class problem show significant improvements in both classification accuracies and the number of features required for all 66 pairs of classes.
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Shailesh Kumar, Joydeep Ghosh, and Melba M. Crawford "Multiresolution feature extraction for pairwise classification of hyperspectral data", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000);

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