Abstract
With the development of sensors, the spatial and spectral resolutions of remote sensing data are getting much higher, which presents new possibilities and challenges for pixel based material classification. When most of the methods available in literature extract features in spectrum domain for land material classification, the rich information contained in hyperspectral data is not fully used. As a result, the classification accuracies reported in literature are not satisfying. In this work, we aim to use joint spatial and spectral analysis technique to extract information about signal variances in space, spectrum and joint space-spectrum domain. The feature thus extracted can better represent the signal variances and can thus improve overall classification accuracy.
Published Version
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