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 7 APPLICATIONS IN FORESTRY

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: A LOCAL CORRELATION APPROACH FOR THE FUSION OF REMOTE SENSING DATA WITH DIFFERENT SPATIAL RESOLUTIONS IN FORESTRY APPLICATIONS. J. Hill, C. Diemer, O. Stöver, Th. Udelhoven

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 achieved at the cost of loosing some original advantages of satellite remote sensing. 1.2. Combined Sensor Resolution and Data Fusion The engineer’s answer to the above-described dilemma is a compromise. In current and planned systems, lower resolution multispectral channels are often complemented by a single higher resolution panchromatic band, which provides a “representation” of the desired resolution for the multispectral channels. With this combined resolution concept, the acquired data volume is kept within acceptable bounds, while additional spatial details are still made available (Table 1). Several sensors with such constellations are already in orbit (SPOT, IRS-ID, Landsat 7), while others are planned (e.g. for 1999 Ikonos-2, Quickbird-1, OrbView-3). Currently, the Indian remote sensing satellite IRS-ID offers 5.8m spatial resolution in the panchromatic range. The planned new satellite generation will provide lm panchromatic and 4m multispectral resolution (because of the data quantity problem, this extremely high resolution allows only restricted swath widths between 8 and 22km only). Instead of using the panchromatic information separately, there is also the option to fuse the high resolution panchromatic band with the low resolution multispectral data to improve the spatial resolution of the latter (Figure 1). The aim of the fusion procedure is to produce high quality data which contains the characteristic of both the multispectral information (object identification) and the spatial detail (object localisation and texture). The “permission” to merge the data is seen in the fact that edge localisation (as detail manifestation) is more or less identical in different spectral bands and “only” varies in strength and polarity (Schowengerdt, 1980; Tom, 1986). 2. CATEGORIZATION OF DATA FUSION TECHNIQUES During the last 20 years, many fusion algorithms were developed and documented. While the data quantity problem and the idea of data fusion stimulated the deployment of combined resolution sensors, the increasing availability of combined resolution data stimulated the further development of fusion algorithms. The intentions of algorithm developers or users are quite different. They encompass the desire for simple visual enhancements, as well as the demand to reconstruct the theoretically “real” high resolution multispectral image (the true image) as well as possible. Fusion techniques may be categorised into 3 groups, depending on how the panchromatic information is used during the fusion procedure. 2.1. Fusion Procedures Using All Panchromatic Band Frequencies This category includes simple (partially old) band-arithmetic techniques, such as addition and multiplication, as well as the commonly used and well known component substitution techniques such as IHS transformation, principal component substitution (PCS) and the more rarely applied regression variable substitution (Haydn et al., 1982; Carper et al., 1990; Shettigara, 1992; Pellemans et al., 1993). Also the Brovey or color normalizing algorithm (Roller and Cox, 1980; Hallada and Cox, 1983), which holds an intermediate position between band-arithmetic and component substitution techniques, can be classified into this group. Because all procedures of this category make use of all frequencies of the panchromatic channel (which include also “spectral” information components related to the lower resolution multispectral images), they may produce spectral distortions in the result, which depend on the degree of global correlation between the panchromatic and the multispectral channels to be enhanced. Especially for NIR or SWIR channels, this global correlation is usually low, such that fusion procedures are bound to fail. Nevertheless, the IHS trans formation and the Brovey algorithm are often used to fuse the panchromatic band with multispectral channels within the visible spectrum (i.e. channels which are highly correlated with the panchromatic band). 2.2. Fusion Procedures Using Selected Panchromatic Band Frequencies Fusion techniques classified in this group overcome the limitation described above, because only the additional high resolution spatial information (i.e., the high frequencies) of the Î multispectral dataset (low resolution) fusion procedure î panchromatic band (high resolution) î multispectral dataset (high resolution) Fig. 1. Concept of data fusion for resolution enhancement (modified after Pradines, 1986).

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