The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 37–41, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-37-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 37–41, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-37-2015

  10 Dec 2015

10 Dec 2015

A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

D. Akbari1, A. R. Safari1, S. Homayouni2, and S. Khazai3 D. Akbari et al.
  • 1Surveying and Geomatics Engineering Dept., College of Engineering, University of Tehran, Tehran, Iran
  • 2Dept. of Geography, University of Ottawa, Ottawa, Canada
  • 3Dept. of Civil Engineering, College of Engineering, Imam Hussein Comprehensive University, Tehran, Iran

Keywords: hyperspectral images, spectral-spatial classification, marker selection, segmentation

Abstract. An effective approach based on the Minimum Spanning Forest (MSF), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM) and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.