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:: ADAPTIVE FUSION OF MULTISOURCE RASTER DATA APPLYING FILTER TECHNIQUES. K. Steinnocher

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 is restricted to bands that are spectrally located within the spectral range of the panchromatic image. An interesting approach has been presented by Zhukov et al. (1995), which is based on the retrieval of spectral signatures, which correspond to constant grey levels in the panchromatic image. The result reveals sub-pixel variations in the multispectral bands, which are associated with grey level variations in the panchromatic image. Most promising are methods that use wavelet transforms for fusion of multiresolution images, as they preserve the spectral characteristics of the fused image to a high extent (Ranchin and Wald, 1993, Garguet-Duport et al., 1996, Yocky, 1996, Wald et al., 1997). In this paper, we will present an alternative method that is based on adaptive filters. 2. METHODOLOGY Fusion of multiresolution optical image data aims at the derivation of multispectral images providing the high spatial resolution of the panchromatic image. The perfect result of such a process would be an image, that is identical to the image the multispectral sensor would have observed if it had the high resolution of the panchromatic sensor (Wald et al., 1997). Such an image would allow differentiating at least all objects that are detectable in the panchromatic image. An approximation of the desired result could be obtained by first extracting the objects from the panchromatic image and „filling“ them with the corresponding average multispectral information. Similar techniques have been successfully used for integrating spectral image data with vector layers in a GIS (Janssen et al., 1990). The drawback of this method for multi-image fusion lies in the requirement of a consistent set of object borders. Although the requirement can be fulfilled through segmentation of the panchromatic image, it would need computationally intensive and therefore time consuming preprocessing. As an alternative, we propose a filtering approach, called Adaptive Image Fusion (AIF), that uses local object edges instead of image segments. 2.1. Sigma filter We assume that an image object Z is represented by a set of neighbouring pixels Zj, whose values are Gaussian distributed, i.e. z ~ N (|i z , o z ). This assumption of normality is generally reasonable for common spectral response distributions (Lillesand and Kiefer, 1994). In the local neighbourhood of an object edge we will therefore find two distributions, each representing one of the neighbouring objects. To separate these objects, i.e. to assign each pixel to one of these objects, adaptive filter techniques can be used. These filters have been successfully applied to image data for noise reduction, in particular for suppression of speckle in SAR imagery. We have chosen a modified sigma filter as it matches the assumptions given above. The sigma filter averages only those pixels in a local window, which lie in a two-sigma range of the central pixel value (Lee 1983). All other pixels are assumed to belong to another distribution i.e. they represent a neighbouring object. As this filter is based on the assumption that the central pixel is in fact the mean of its Gaussian distribution, it might not include all relevant pixels in the averaging process. Therefore, a more general approach, namely the modified sigma filter, was presented by Smith (1996). It averages all pixels, which could belong to the same distribution as the central pixel without knowing the actual mean of this distribution. Application of the modified sigma filter requires the estimation of the normalised standard deviation which can be based on empirical analysis of the panchromatic image (Smith 1996). First, a local average and a local standard deviation image are computed from the original image, using the window size chosen for the filter process. Next, the standard deviation image is divided by the average image on a pixel basis resulting in a normalised standard deviation image. The mode of the histogram of this image is an adequate estimate to start with, although test runs have shown that in most cases it has to be reduced to get the desired results. When applying the modified sigma filter, areas with a low standard deviation, i.e. areas containing one object, will be smoothed. Within areas of high standard deviation, i.e. areas containing object edges, only those pixels will be averaged that belong to the same distribution as the central pixel. 2.2. Adaptive image fusion For the fusion approach, the multispectral bands are included in the filtering process. As most fusion techniques, AIF requires co-registration of the panchromatic and the multispectral images and nearest neighbor resampling of the multispectral bands to the higher spatial resolution. There is no need of applying a higher level resampling process, such as cubic interpolation, as the blockiness of the lower resolution images will be eliminated during the fusion process. In the following, the new high resolution pixels of the multispectral bands are addressed as sub pixels. The AIF algorithm starts with applying a modified sigma filter to the panchromatic image. At each position of the moving window the two sigma range related to the central pixel is calculated, and all pixels within the window which fall into that range are selected. The position of the selected pixels is then transferred to the multispectral band, where the averaging of the respective sub-pixels is performed. The process could be described as sigma filtering of the multispectral band where the filter behavior is controlled by the panchromatic image. It is important to note that no spectral information is transferred from the panchromatic image to the multispectral band during the whole procedure. This leads to a better delineation of objects in the multispectral band without significantly changing the spectral information. The effect of the AIF is shown in Figure 1. The figure on the left side represents an (idealised) panchromatic image, the figure in the center the respective (idealized) multispectral band. It is obvious that the spectral correlation between the two images is relatively low (as occurs e.g. with a panchromatic image and a near infrared channel). By applying the AIF iteratively, the mixed pixels of the multispectral band will be separated step by step into the single objects they are composed of. The right

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