| タイトル | Advances in Spectral-Spatial Classification of Hyperspectral Images |
| 著者（英） | Chanussot, Jocelyn; Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Fauvel, Mathieu |
| 著者所属（英） | NASA Goddard Space Flight Center |
| 発行日 | 2012-04-26 |
| 言語 | eng |
| 内容記述 | Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods. |
| NASA分類 | Earth Resources and Remote Sensing |
| レポートNO | GSFC.JA.0036.2012 |
| 権利 | Copyright |
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