Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Abstract

Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21.000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.

High-resolution airborne imaging DOAS measurements of NO<sub>2</sub> above Bucharest during AROMAT

“…1. The largest values are found above bright rooftops, which is in qualitative agreement with surface reflectances for urban surfaces in the literature (Herold et al, 2004;Heiden et al, 2007).…”

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

“…As outer physical effects influence aerial survey, the spectrum is slightly different from the spectrum obtained in laboratory circumstances (Heiden, U. et al 2007). The settings of the measurements are device specific and depend on the demands of the users (Jung, A. et al 2012).…”

Urban land cover classification using hyperspectral data

“…Land use and land cover classification accuracy in urban areas is limited owing to the broad spectral resolution of multispectral satellite data and spatial heterogeneity. However, advances in imaging spectrometers have begun to fill the gap in multispectral sensor limitations (Heiden et al, 2007).…”

“…In this context, hyperspectral data enable more accurate discrimination of urban features (Tan and Wang, 2007). In addition to classifying whole image into a set of classes, presence of numerous bands enable detailed feature identification and characterization of surface materials (Bokoye, andDionne, 2004 andHeiden et al, 2007).…”