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 The Brovey Transform (Hallada and Cox, 1983), a special combination of arithmetic combinations including ratio, is a formula that normalises multispectral bands used for a RGB display, and multiplies the result by any other desired higher resolution image to add the intensity or brightness component to the image. The algorithm is shown in Eq. 5 where DNfu Sed means the DN of the resulting fused image produced from the input data in ‘n’ multispectral bands b l5 b 2 , ... b n multiplied by the high resolution image DNhighres. DN bi * ^ xr (5) DN fused ~ DN fel + DN b2 + ... + DN bn DN highres 3.4. Principal Component Analysis PCA is a statistical technique that transforms a multivariate dataset of correlated variables into a dataset of new uncorrelated linear combinations of the original variables. The approach for the computation of the principal components (PCs) comprises the calculation of: 1. covariance (unstandardised PCA) or correlation (standardised PCA) matrix 2. eigenvalues, eigenvectors 3. PCs An inverse PCA transforms the combined data back to the original image space. Replacing the first principal component with a higher resolution intensity image, a multi-channel dataset can be transformed into a spatial resolution image of higher ground resolution. This is called Principal Component Substitution - PCS (Shettigara, 1992). The idea of increasing the spatial resolution of a multi-channel image by introducing an image with a higher resolution. The channel, which will replace PCI, is stretched to the variance and average of PCI. The higher resolution image replaces PCI, since it contains the information which is common to all bands while the spectral information is unique for each band (Chavez et al., 1991). PCI accounts for maximum variance, which can maximise the effect of the high resolution data in the fused image (Shettigara, 1992) . 3.5. Wavelets Wavelets, a mathematical tool developed originally in the field of signal processing, can also be applied to fuse image data, following the concept of the multi-resolution analysis (MRA). The wavelet transform creates a summation of elementary functions (= wavelets) from arbitrary functions of finite energy. The weights assigned to the wavelets are the wavelet coefficients, which play an important role in the determination of structure characteristics at a certain scale in a certain location. The interpretation of structures or image details depends on the image scale, which is hierarchically compiled in a pyramid produced during the MRA (Ranchin and Wald, 1993) . Once the wavelet coefficients are determined for the two images of different spatial resolution, a transformation model can be derived to determine the missing wavelet coefficients of the lower resolution image. Using these, it is possible to create a synthetic image from the lower resolution image at the higher spatial resolution. This image contains the preserved spectral information with the higher resolution, hence showing more spatial detail. This method is called ARSIS, an abbreviation of the French definition “amélioration de la résolution spatial par injection de structures” (Ranchin et al., 1996). 3.6. Regression Variable Substitution Multiple regression derives a variable, as a linear function of multi-variable data that will have maximum correlation with univariate data. In image fusion the regression procedure is used to determine a linear combination (replacement vector) of image channels that can replace an existing image channel. If the channel to be replaced is one of the lower resolution input bands, this procedure leads to an increase of spatial resolution. To achieve the effect of fusion, the replacement vector should account for a significant amount of variance or information in the original multivariate dataset. The method can be applied to spatially enhance data. In case of fusion of SPOT XS and PAN channels, for each pixel location three new values are computed to produce the 10 m multispectral pixels based on the known relationship between PAN and XS. The linear regression is then calculated for each channel combination, i.e. XS green band - PAN, XS red band - PAN and IR band - PAN. 4. RESOLUTION MERGE CHALLENGES The resolution merge is relatively straightforward, when using data from the same satellite, e.g. SPOT PAN & XS, IRS-1C PAN & LISS, etc. But it is also applicable to imagery originating from different satellites carrying similar sensors, e.g. SPOT XS & 1RS-1C PAN. Some of the approaches are already implemented in commercial-off-the-shelf (COTS) software packages, e.g. PCI Geomatics and ERDAS IMAGINE. These include amongst others multiplication techniques, PCA and Brovey transform. Image providers already integrated resolution merged products into their catalogue of standard products. Examples are SPOT IMAGE (1999) and SSC Satellitbild (1999). However, very often the user has to fine-tune individual parameters of the fusion process. A good example is the use of arithmetic combinations, which allow the user to put different weights on the input images in order to enhance application relevant features in the fused product. The major difficulty is the co-registration of images with large differences in spatial resolution. The identification of tie points can cause problems in both datasets: ■ Multispectral data - difficulty of identifying corresponding points due to the lower resolution; ■ Panchromatic data - shadow effect caused by buildings or similar objects due to high level of detail. Especially in the case of spatial resolution ratios of up to 1:10, i.e. SPOT XS and Russian imagery, points or features have to be selected with care, due to the additional large difference in viewing geometry of the sensors involved. An integrated approach is the use of sensor models, which provide a re

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