Name
Title | ||
|---|---|---|
Segmentation and classification of hyperspectral images using watershed transformation |
Abstract | ||
|---|---|---|
Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.01.016 | Pattern Recognition |
Keywords | Field | DocType |
classification map,watershed transformation,new spectral-spatial classification scheme,vector machines classification,classification accuracy,classification process,classification result,pixel-wise classification,hyperspectral image,classification performance,accurate classification,support vector machine,watershed transform,reference data,segmentation,spectrum,mathematical morphology,classification,spatial information,feature extraction,watershed,majority voting,watershed segmentation | Spatial analysis,Pattern recognition,Mathematical morphology,Segmentation,Support vector machine,Image processing,Hyperspectral imaging,Feature extraction,Pixel,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 7 | Pattern Recognition |
Citations | PageRank | References |
188 | 6.83 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Tarabalka | 1 | 391 | 13.93 |
| Jocelyn Chanussot | 2 | 4145 | 272.11 |
| J. A. Benediktsson | 3 | 860 | 83.81 |