XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: PANSHARPENING OF HYPERSPECTRAL IMAGES IN URBAN AREAS Chembe Chisense, Johannes Engels, Michael Hahn and Eberhard Gülch

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia PANSHARPENING OF HYPERSPECTRAL IMAGES IN URBAN AREAS Chembe Chisense, Johannes Engels, Michael Hahn and Eberhard Gülch Stuttgart University of Applied Sciences Schellingstr. 24 D-70174 Stuttgart, Germany johannes.engels@hft-stuttgart.de Commission VII/6 KEY WORDS: image fusion, hyperspectral, resolution, urban, classification ABSTRACT: Pansharpening has proven to be a valuable method for resolution enhancement of multi-band images when spatially high-resolving panchromatic images are available in addition. In principle, pansharpening can beneficially be applied to hyperspectral images as well. But whereas the grey values of multi-spectral images comprise at most relative information about the registered intensities, calibrated hyperspectral images are supposed to provide absolute reflectivity values of the respective material surfaces. This physical significance of the hyperspectral data should be preserved within the pansharpening process as much as possible. In this paper we compare several common pansharpening methods such as Principal Component Fusion, Wavelet Fusion, Gram-Schmidt transform and investigate their applicability for hyperspectral data. Our focus is on the impact of the pansharpening on material classifications. Apart from applying common quality measures, we compare the results of material classifications from hyperspectral data, which were pansharpened by different methods. In addition we propose an alternative pansharpening method which is based on an initial segmentation of the panchromatic image with an additional use of map vector data. 1 INTRODUCTION Pansharpening is a well-established technique for the enhance- ment of spatial image resolution, which allows the fusion of a low resolution multiband image with a high resolution panchromatic image. A mere upsampling of the multi-band image with an in- terpolation filter would result in a blurry quality with smoothed edges and missing short-wavelength constituents in the spatial Fourier expansions. Therefore at least the short-wavelength spa- tial information of the panchromatic image is integrated within fusion. This process, however, entails quite an amount of arbi- trariness. Whereas the overall brightness of a pixel (apart from histogram matching) can be more or less directly adopted from the panchromatic information, the ratio of the grey values of the individual channels depends strongly on the respective pansharp- ening method. More recently developed sensors suggest the application of pan- sharpening techniques, as those sensors frequently provide data in different resolution levels. Even more significant is the differ- ence between hyperspectral sensors and colour cameras. Hyper- spectral data usually feature a lower resolution than RGB images. However, pansharpening of hyperspectral imagery by a panchro- matic image imposes two distinct problems: e Often the ^panchromatic" image does not comprise the full wavelength range of the hyperspectral image but only a part of it. The panchromatic image might e.g. be derived from RGB imagery, whereas the hyperspectral image covers a wider range from the visible up to the shortwave infrared. In such a case, the panchromatic image is not "representative", i.e. not a pixelwise average of the hyperspectral image. e Calibrated hyperspectral data ideally feature reflectivity val- ues of the respective material surfaces (disregarding depen- dencies on the source - reflector - sensor geometry, i.e. the BRDF function). Therefore, in contrast to common imagery, an absolute hyperspectral grey value carries physical signif- icance as a distinctive parameter of the material surfaces alone. Any "resolution enhancement" runs the risk of di- luting this significance by a distortion of the parameter. In the present paper we compare several common pansharpening methods with respect to their impact or suitability for hyperspec- tral data. We present in addition an alternative pansharpening method, which is based on the segmentation of the panchromatic image. The interpolation on the finer grid is performed by us- ing data points from the same segment only. For the evaluation we apply visual inspection, profile analysis and some common quality measures. As more important, however, we rate a com- parison of the classification results which are achieved from the pansharpened images. 2 ESTABLISHED PANSHARPENING METHODS In the last three decades, a lot of algorithms have been devel- oped which are, however, based on only few elementary prin- ciples. In any case, high-frequent panchromatic information is merged into spatially low resolution but spectrally differentiated data. We briefly outline the principles of some of the most com- mon pansharpening methods. Here we follow the more detailed representation in (Pohl and van Genderen 1998) or (Hirschmugl et al. 2005). 2-1 Gram-Schmidt Fusion The Gram-Schmidt Fusion works pixelwise. It was developed and described in detail by (Laben and Brower 2000). In a first step, a low resolution panchromatic channel is constructed as a weighted average of the original hyperspectral channels. Based on this first new channel, subsequently further linear combina- tions are formed by orthogonalization of the original bands with

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