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 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: OPERATIONAL APPLICATIONS OF MULTI-SENSOR IMAGE FUSION. C. Pohl, H. Touron

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 124 data, the images have to be geometrically and radiometrically corrected, before being suitable for the fusion process, using collateral data such as atmospheric conditions, sensor viewing geometry, ground control points (GCPs), etc. (Pohl, 1996). An elementary pre-processing step is the accurate co-registration of the dataset, so that corresponding features coincide. A general description of the necessary pre-processing steps can be found in Cheng et al. (1995), Toutin (1994) and Richter (1997). An overview on the concepts of fusion is given in Wald (1998a). Fig. 1. Overall data fusion process in remote sensing. Depending on the processing stage at which data fusion takes place, it is distinguished between three different fusion levels: • pixel, • feature, and • decision level. Image fusion mostly refers to pixel-based data fusion, where the input data is merged applying a mathematical algorithm to the coinciding pixel values of the various input channels to form a new output image. The concept of the different fusion levels is described in more detail in Pohl and van Genderen (1998) and Pohl (1998). An overview on the different definitions of data and image fusion is available in Wald (1998b). Once the alignment of the dataset is established, it is possible to apply certain fusion techniques. The manifold fusion techniques can be grouped into 1. Colour related techniques and 2. Statistical/numerical approaches (Pohl, 1996). The first group comprises methods that refer to the different possibilities of representing pixel values in colour spaces. An example is the Intensity (I) - Hue (H) - Saturation (S) colour transformation. The IHS technique intends to separate different characteristics of colour perception by the human interpreter. The intensity relates to the brightness, hue represents the dominant wavelength, whilst the saturation is defined by the purity of the colour (Gillespie et al., 1986). If a multispectral image is transformed from the RGB space into HIS, it is possible to integrate a fourth channel exchanging it with one of the elements obtained (I, H or S). There are many other techniques that follow the substitution principle. A description can be found in Shettigara (1992). Of course, there are other colour transformations which suit the fusion concept (e.g. RGB or Luminance/Chrominance - YIQ). The second group of fusion techniques deals amongst others with arithmetic combinations of image channels, Principal Component Analysis (PCA) (Singh and Harrison, 1985) and Regression Variable Substitution (RVS) (Shettigara, 1992; Singh, 1989). Fusion by band combinations using arithmetic operators opens a wide range of possibilities to the remote sensing data user. Image addition or multiplication contribute to the enhancement of features, whilst channel subtraction and ratios allow the identification of changes (Mouat et al., 1993). The Brovey transformation forms a particular method of ratioing, preserving spectral values, while increasing the spatial resolution. The PCA and similar methods serves the reduction of data volume, change detection or image enhancement. RVS is used to replace bands by linearly combining additional image channels with the dataset. An overview of existing techniques, as well as a comprehensive description of their use is given in the review by Pohl and van Genderen (1998). 3. APPLICATIONS OF IMAGE FUSION Image fusion is used in a broad variety of applications: geology, landuse / agriculture / forestry, change detection, map updating, hazard monitoring, just to name a few. However, in many cases it has not reached an operational status, due to the difficulty of generalizing image combinations and fusion processes. Due to the scarcity of simultaneously acquired satellite imagery, most image fusion applications carry a multi-temporal component. In some cases, it is used in the framework of monitoring (change detection); in others it is an unavoidable constraint and has to be considered in the evaluation of the resulting fused product. A very important factor of applying image fusion is the integration of complementary data. The complementarity of visible and infrared (VIR) with synthetic aperture radar (SAR) images is a well known example, where the objects contained in the images are ’seen’ from very different perspectives (wavelength and viewing geometry). The integration of high resolution and multispectral information forms another type of complementarity. The following sections provide an overview of issues in operationally-used image fusion relating to the processing involved and discuss benefits and limitations of approaches, illustrated by applications. All results have to be viewed in the context of visual image exploitation. 3.1. Resolution Merge One of the more established approaches is the so-called resolution merge. It aims at the integration of lower resolution multispectral with higher resolution panchromatic data in order to benefit from high spectral and spatial resolution in one image. It is relatively straightforward, when using data from the same satellite, e.g. SPOT PAN & XS, IRS-1C PAN & LISS, etc. But it is as well applicable to imagery originating from different satellites carrying similar sensors (e.g. SPOT XS &

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