Marker-based Hierarchical Segmentation and classification approach for hyperspectral imagery

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral 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. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, a map of markers is constructed using classification results. Then, a novel constrained M-HSEG algorithm is applied. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Yuliya Tarabalka, James C. Tilton, Jon Atli Benediktsson, Jocelyn Chanussot
• Keywords: Hierarchical Step-wise Optimization, Hyperspectral Image, Hyperspectral Image Analysis, ICASSP 2011, Signal Processing