Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm |
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| Authors: | A Senthil Kumar V Keerthi AS Manjunath Harald van der Werff Freek van der Meer |
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| Institution: | 1. National Remote Sensing Centre, Data Processing Area, Balanagar, Hyderabad 500 625, India;2. International Institute for Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, 7500AA Enschede, The Netherlands |
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| Abstract: | Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously. |
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| Keywords: | Hyperspectral image Spectral curve matching Multiresolution analysis Mixed pixel classification |
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