RULE-BASED CLASSIFICATION OF A HYPERSPECTRAL IMAGE USING MSSC HIERARCHICAL SEGMENTATION

• Main Authors: D. Akbari, A. R. Safari
• Format: Article
• Language: English
• Published: Copernicus Publications 2013-09-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W3/13/2013/isprsarchives-XL-1-W3-13-2013.pdf

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. In this paper, we propose to use spectral-spatial classifiers at the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Then, a novel marker-based HSEG algorithm (that is called Multiple Spectral-Spatial Classifier-HSEG (MSSC-HSEG)) is applied, resulting in a segmentation map. The segmentation results are then used in a rule-based classification using spectral, geometric, textural, and contextual information. The experimental results, presented for a hyperspectral airborne image, demonstrate that the proposed approach yields accurate segmentation and classification maps, when compared to previously classification techniques.