A comparison of classification methods for analysis of remotely sensed hyperpectral data

This paper examines the utility of hyperspectral imagery for remote sensing data analysis. The acquired data volumes are very important, often reaching hundreds or thousands of channels for a single scene observed. Certainly, the large quantity contained in the hyperspectral database is accompanied by a complex physical content and consequently a considerable time computing which can affect the quality of treatment. These channels come from a very fine spectral sampling, allow discriminating and differentiating constituents that are spectrally close. Furthermore, hyperspectral imaging is a technique that is as strong potential initiating new research in the development of mathematical morphology. This theory, mainly inspired by the image processing problems, extends to a new more complex scope of which is hyperspectral mathematical morphology. Applied to hyperspectral data in hyperdimensional features spaces, we compare two proposed classification approaches. The first method is based on centralized segmentation methodology which exploits the information complementarities. The second method is based on hierarchical clustering which consists on combining a divisive clustering approach applied at a high-level and an agglomerative clustering operating at a low-level.