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:: SENSOR FUSED IMAGES FOR VISUAL INTERPRETATION OF FOREST STAND BORDERS. R. Fritz, I. Freeh, B. Koch, Chr. Ueffing

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 185 1.1. Digital forest inventory mapping Actual stand borders are delineated with the aid of BAV orthophotos, generated from periodically acquired imagery for updating topographic maps. The low spectral response of the panchromatic images are not appropriate for delineating forest stand borders. 1.2. High resolution satellite data Nearly every nation active in space technology has the tendency of developing high resolution optical satellite stereo systems (Konecny and Schiewe, 1997). Recently, high resolution optical satellite data is made available for civil applications and the trend will definitely improve in near future. The era of high resolution satellite data already started with the launch of the Indian satellite IRS-1C in 1995. The expected start of the commercial era of high resolution Earth Observing satellites at the end of the year 1997 (Fritz, 1996) was delayed up to now due to failed missions (EarlyBird®). The recently launched IKONOS-1 also failed. Space Imaging® is however confident that with the launch of IKONOS-2 they will achieve their goal. The IKONOS-2 mission will be followed by the missions of QuickBird® and OrbView®-3 later this year with a spatial resolution between 1 and 4m (see Table 1). Most of the systems do not only provide a very high spatial resolution but also stereo capability, which offers the possibility not only to extract two-dimensional spatial information but also height information. In addition, a large number of satellites with similar capabilities will also reduce the weather dependency due to the more frequent area coverage by all satellites together. Some people expect that operation of the above mentioned high resolution satellite systems will substitute many applications of aerial photography. In particular, it is expected that the price of satellite products will be lower than those derived from aerial photography (Fritz, 1996). In addition, satellite data provide direct digital use and integration in existing digital databases. 1.3. Sensor fusion Due to technical reasons, optical satellite data have either a high spectral or a high spatial resolution. In order to use the benefit of both, several data fusion techniques have been developed to integrate information from different datasets in one dataset. Sensor Fusion is a part of the wider defined term Data Fusion. The following definition of Data Fusion was adopted by the EARSeL - SEE - EMP working group (Wald, 1998): "data fusion is a formal framework in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of ’greater quality’ will depend upon the application". This definition emphasizes explicitly data from different sources. If these data are from imaging sensors, the fusion is described by the term Sensor or Image Fusion. A definition is given by van Genderen and Pohl (1994): " Image Fusion is the combination of two or more different images to form a new image by using a certain algorithm." At the end of the 70s, Dailly et al. (1979) tried to combine Landsat MSS and SIR-A radar data and Chavez et al. (1986) fused Landsat TM and panchromatic aerial photographs. A list of fusing different image types is given by Jensen (1996) and Pohl (1996). Standard pixel-based image fusion procedures (Pohl and van Genderen, 1998) like IHS, PC A and Brovey have been applied System IRS-1C+D MOMS-2P Earth Watch Space Imaging Orbital Sciences Characteristics ’QuickBird’ Tkonos-2’ ’OrbView-3’ Panchromatic 0.50 - 0.75 0.52 - 0.76 0.45 - 0.90 0.50 - 0.90 0.45 - 0.90 Spectral bands: Blue 0.449-0.511 0.45 - 0.52 0.45 - 0.52 0.45 - 0.52 Green 0.52 - 0.59 0.532 - 0.576 0.53 - 0.59 0.52 - 0.60 0.52 - 0.60 Red 0.62 - 0.68 0.645 - 0.677 0.63 - 0.69 0.63 - 0.69 0.63 - 0.69 NIR 0.77 - 0.86 0.772-0.815 0.77 - 0.90 0.76 - 0.90 0.76 - 0.90 SWIR 1.55 - 1.70 (1.55 - 1.75) Resolution (m) Pan (Nadir) 5.8 6 1+2 1 1 Spectral 23.5 18 4 4 4 Stereo capability No Along track Along track Along track Along track Orbit Sun-syn. +/- 28.5 ° Sun-syn. Sun-syn. Sun-syn. Flying height (km) 817 ca. 296 600 680 470 Scene coverage 70x70(p) 78 6x6 / 30x30 60x60 15x15 (km 2 ) 142x142 (m) Mission duration 1994-2000 1995-1999 from 1999 from 1999 from 1999 Repetition (days) 24 16-20 14 14-16 Table 1. Description of present and near-future high resolution satellite systems (spectral bands in pm).

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