Abstract
Abstract. 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.
Highlights
Imaging spectroscopy (Goetz, 1985), known as hyperspectral (HS) imaging, is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene at a short, medium or long distance by an airborne or satellite sensor
One can separate pixel-wise processing techniques that work on the spectral information only (one of the most frequently used techniques are Support Vector Machines (SVM) (Camps-Valls, 2005a)) and spectral-spatial classification techniques that take into consideration both the spectra of the pixels and their spatial context (Fauvel, 2008).The importance of analyzing spatial and spectral patterns simultaneously has been identified as a desired goal by many scientists devoted to multidimensional data analysis
Experimental results are demonstrated on a HS airborne image acquired by the Reflective Optics System Imaging Spectrometer (ROSIS)
Summary
Imaging spectroscopy (Goetz, 1985), known as hyperspectral (HS) imaging, is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium or long distance by an airborne or satellite sensor. One can separate pixel-wise processing techniques that work on the spectral information only (one of the most frequently used techniques are Support Vector Machines (SVM) (Camps-Valls, 2005a)) and spectral-spatial classification techniques that take into consideration both the spectra of the pixels and their spatial context (Fauvel, 2008).The importance of analyzing spatial and spectral patterns simultaneously has been identified as a desired goal by many scientists devoted to multidimensional data analysis. In the seminal works on spectral-spatial image classification, the information from the closest neighborhoods, defined by either fixed windows (Kettig, 1976 and Camps-Valls, 2006b) or morphological profiles (Fauvel, 2008), has been considered for classifying each pixel. A Minimum Spanning Forest (MSF) rooted on the selected markers was constructed, resulting in a segmentation and classification map The drawback of this method is that the choice of markers strongly depends on the performances of the selected pixel-wise classifier.
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