Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: TOOLS AND METHODS FOR FUSION OF IMAGES OF DIFFERENT SPATIAL RESOLUTION. C. Pohl

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 TOOLS AND METHODS FOR FUSION OF IMAGES OF DIFFERENT SPATIAL RESOLUTION C. Pohl International Institute for Aerospace Survey and Earth Sciences (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands, pohl@itc.nl KEYWORDS: Image fusion, pixel level, resolution merge, spatial resolution, fusion techniques. The usefulness of remote sensing data, in particular of images from Earth observation satellites, largely depends on their spectral, spatial and temporal resolution. Each system has its specific characteristics providing different types of parameters on the observed objects. Focussing on operational and most commonly used commercial remote sensing satellite sensors, this paper describes how image fusion techniques can help increase the usefulness of the data acquired. There are plenty of possibilities of combining images from different satellite sensors. This paper concentrates on the existing techniques that preserve spectral characteristics, while increasing the spatial resolution. A very common example is the fusion of SPOT XS with PAN data to produce multispectral (3- band) imagery with 10 m ground resolution. These techniques are also referred to as image sharpening techniques. A distinction has to be made between the pure visual enhancement (superimposition) and real interpolation of data to achieve higher resolution (e.g. wavelets). In total, the paper describes a number of fusion techniques, such as RGB colour composites, Intensity Hue Saturation (IHS) transformation, arithmetic combinations (e.g. Brovey transform), Principal Component Analysis, wavelets (e.g. ARSIS method) and Regression Variable Substitution (RVS), in terms of concepts, algorithms, processing, achievements and applications. It is mentioned in which way the results of various techniques are influenced by using different pre-processing steps as well as modifications of the involved parameters. All techniques are discussed and illustrated using examples of applications in the various fields that are part of ITC’s educational programme and consulting projects. ABSTRACT 1. INTRODUCTION higher resolution (e.g. wavelets); the latter being proposed amongst others by Mangolini (1994) and Ranchin et al. (1996). According to the EARSeL 1 Special Interest Group on Data Fusion (Data Fusion SIG) data fusion is defined as "... a formal framework in which means and tools are expressed for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of greater quality will depend upon the application" (Wald, 1998). Image fusion forms a subgroup within this definition and aims at the generation of a single image from multiple image data for the extraction of information of higher quality. Having that in mind, the achievement of high spatial resolution while maintaining the provided spectral resolution falls exactly into this framework. Using appropriate fusion techniques high spatial resolution panchromatic imagery can be combined with multispectral imagery of lower resolution. In this way, the spectral resolution may be preserved, while a higher spatial resolution is incorporated, which represents the information content of the images in much more detail (Franklin and Blodgett, 1993; Pellemans et al., 1993). A special case is the fusion of channels from a single sensor for resolution enhancement, e.g. TM data. The lower resolution thermal channel can be enhanced using the higher resolution spectral channels (Moran, 1990). Other approaches increase the spatial resolution of the output channel using a windowing technique on the six multispectral bands of TM (Sharpe and Kerr, 1991). The fusion of SAR/VIR does not only result in the combination of disparate data but may also be used to spatially enhance the imagery involved (Welch, 1984). Geometric accuracy and increase of scales using fusion techniques is of concern to mapping and updating (Jutz, 1988; Chiesa and Tyler, 1990; Pohl, 1996). 2. RESOLUTION MERGE The concept of combining images with complementary information opens a broad field of applications. There is a vast variety of techniques to combine images from different sensors. However, this paper focuses on image fusion techniques that preserve spectral characteristics whilst increasing spatial resolution to provide images of greater quality. A very common example is the fusion of SPOT XS with PAN data to produce multispectral (3-band) imagery with 10 m ground resolution. These techniques are also referred to as image sharpening techniques and often called resolution merge. A distinction has to be made between the pure visual enhancement (superimposition) and real interpolation of data to achieve 3. IMAGE FUSION TECHNIQUES European Association of Remote Sensing Laboratories. Image fusion for spatial resolution enhancement is performed at pixel level as one of the three fusion levels defined by Pohl and van Genderen (1998). It requires the accurate co-registration of

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