期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2013
卷号：XL-1/W3
页码：13-18
DOI：10.5194/isprsarchives-XL-1-W3-13-2013
出版社：Copernicus Publications
摘要：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
