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 108 ADAPTIVE FUSION OF MULTISOURCE RASTER DATA APPLYING FILTER TECHNIQUES K. Steinnocher Austrian Research Centers, Seibersdorf, Environmental Planning, A-2444 Seibersdorf, klaus.steinnocher@arcs.ac.at KEYWORDS: Adaptive Filters, Image Fusion, Image Sharpening, Multiresolution Data, Pre-Segmentation. ABSTRACT Current remote sensors offer a wide variety of image data with different characteristics in terms of temporal, geometric, radiometric and spectral resolution. Although the information content of these images might be partially overlapping, the complementary aspects represent a valuable improvement for information extraction. To exploit the entire content of multisensor image data, appropriate techniques for image fusion are indispensable. The objective of this paper is to analyse the benefits that can be gained from the adaptive image fusion (AIF) method. This method allows the fusion of geometric (spatial) and thematic (spectral) features from multisource raster data using adaptive filter algorithms. If applied to multiresolution image data, it will sharpen the low spatial resolution image according to object edges found in the higher spatial resolution image. In contrast to substitution methods, such as Intensity-Hue-Saturation or Principal-Component Merging, AIF preserves the spectral characteristics of the original low resolution image. Thus, it supports applications that rely on subsequent numerical processing of the fused image, such as multispectral classification. However, it may also be used in combination with substitution merging methods leading to an improved product for visual interpretation. In this case, the AIF will be applied in order to sharpen the original low spatial resolution image before performing the substitution. From the various applications that could benefit from AIF, three examples are presented: improving the delineation of forest areas, sharpening of agricultural fields and monitoring of urban structures. In all cases, the fusion leads to an improved segmentation and a more precise estimation of the area of single land cover objects. 1. INTRODUCTION Image fusion in a general sense can be defined as “the combination of two or more different images to form a new image by using a certain algorithm“ (Van Genderen and Pohl, 1994). It aims at the integration of all relevant information from a number of single images into one new image. From an information science point of view, image fusion can be divided into three categories depending on the abstraction level of the images: pixel, feature and decision based fusion. On the pixel level, the fusion is performed on a per-pixel basis. This category encompasses the most commonly used techniques (Vrabel 1996). The second level requires the derivation of image features, which are then subject to the fusion process. Decision based fusion combines either pre-classified data derived separately from each input image or data from multiple sources in one classification scheme (Benediktsson and Swain, 1992, Schistad Solberg et al. 1994). An alternative grouping of image fusion techniques refers to the different temporal and sensor characteristics of the input imagery. The combination of multitemporal - single sensor images represents a valuable basis for detecting changes over time (Singh, 1989, Weydahl, 1993, Kressler and Steinnocher, 1996). Multisensor image fusion combines the information acquired by different sensor systems, to benefit from the complementary information inherent in the single image data. A representative selection of studies on multisensor fusion, comprising a wide range of sensors, is given by Pohl and van Genderen (1998). Within this group, a focus can be found on the fusion of optical and SAR data (Harris et al., 1990, Schistad Solberg et al., 1994) and of optical image data with different spectral and spatial resolutions (Chavez et al., 1991, Pellemans et al., 1993, Shettigara, 1992, Zhukov et al., 1995, Garguet- Duport et al., 1996, Yocky, 1996, Vrabel, 1996, Wald et al. 1997). In the remainder of this paper, we will concentrate on the fusion of multisensor optical image data with different spatial and spectral resolutions. High resolution data sets of this kind are typically acquired from single platforms carrying two sensors in the optical domain - one providing panchromatic images with a high spatial resolution, the other providing multispectral bands (in the visible and near infrared spectrum) with a lower spatial resolution. Current examples of these platforms are SPOT3/4, IRS-1 C/D, and the just recently launched Landsat 7. For the near future a number of satellites with similar characteristics are announced (Carlson and Patel, 1997). The motivation for merging a panchromatic with multispectral images lies in the increase of details while preserving the multispectral information. The result is an artificial multispectral image stack with the spatial resolution of the panchromatic image. Common methods to perform this task are arithmetic merging procedures or component substitution techniques such as the Intensity-Hue-Saturation (IHS) or the Principal Component Substitution procedures (Carper et al., 1990, Chavez et al., 1991, Shettigara, 1992). These techniques are valuable for producing improved image maps for visual interpretation tasks, as they strongly enhance textural features. On the other hand, they can lead to a significant distortion of the radiometric properties of the merged images (Vrabel, 1996). Pellemans et al. (1993) introduced the radiometric method, where the new multispectral bands are derived from a linear combination of multispectral and panchromatic radiances. While this method keeps the radiometry of the spectral information, it

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