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 191 A LOCAL CORRELATION APPROACH FOR THE FUSION OF REMOTE SENSING DATA WITH DIFFERENT SPATIAL RESOLUTIONS IN FORESTRY APPLICATIONS J. Hill, C. Diemer, O. Stover, Th. Udelhoven Remote Sensing Department, University of Trier, Behringstrasse, Trier, D-54286, Germany hill@fews05.uni-trier.de, c.diemer@netcologne.de KEYWORDS: Data Fusion, Resolution Enhancement, Local Correlation Modelling, Remote Sensing, Forestry. ABSTRACT Until now, satellite data are only of limited use to Mid-European forest management. A major limitation is the low spatial resolution of the commonly available satellite sensors. In this paper, we present a specific data fusion approach (local correlation modelling) which can be used to produce multispectral images with high spatial resolution based on panchromatic reference channels. Such data are provided by operational satellite systems (SPOT, IRS-ID, Landsat 7), and their availability may further increase with the advent of new commercial satellite systems. Airborne experimental data were used to assess the quality of the modelling approach discussed in this contribution, compared to traditionally used fusion algorithms (e.g. Brovey, IHS, PCA, filter techniques). Our validation results indicate that local correlation modelling (LCM) performs in all channels significantly better, because the introduced texture is locally adjusted to the conditions of each channel. Local contrast differences between a (degraded) panchromatic band and multispectral channels are adaptively modelled into the fusion result, even if the local relation between the datasets exhibits an inverse contrast polarity. 1. INTRODUCTION 1.1. Satellite Data and the Resolution Dilemma Until now, spacebome remote sensing data have only been of limited use to Mid-European forest management. A major limitation is the low spatial resolution of satellite data compared to that of the traditionally used aerial photographs. On the other hand, satellite remote sensing provides substantial advantages, such as high spectral resolution and synoptic coverage of large areas. The digital data format allows direct digital processing of images and the integration with GIS thematic layers. Another important aspect is that remote sensing data can be understood as a physical measurement, such that a quantitative analysis becomes possible. In view of the growing needs for forest information on one hand (forest damage, new forest structures), and the necessity to reduce costs for inventories on the other hand, these advantages are significant. Accordingly, the demand for satellite data with higher spatial resolution has increased considerably. Current sensor technology allows the deployment of civil high resolution satellite sensors, but we find that an increase in spatial resolution also leads to an exponential increase in data quantity (which becomes particularly important when multispectral data should be collected). Since the amount of data collected by a sensor has to be balanced against the state- of-the-art capacity in transmission rates, archiving and processing capabilities (Colvocoresses, 1977), this leads to the following dilemma: because of limited data volumes, an increase in spatial resolution must be compensated by a decrease in other data sensitive parameters, e.g. swath width, spectral and radiometric resolution, observation and data transmission duration. The reduced swath width also leads to a decrease in temporal resolution (which might eventually be reduced by multiple and tiltable systems). Summarising, improving a satellite sensor’s spatial resolution may only be Sensor Spatial resolution (m) Swath width (km) Temporal resolution (days) max min (across-track) pan blue green red NIR SWIR TIR pan multispectral TM 7 15 30 30 30 30 30 120 185 185 16 - SPOT 4 10 - 20 20 20 20 - 60 60 26 1-5 1RS-ID 5.8 - 23 23 23 70 - 70 142 24 5 MOMS 2P* 6 18 18 18 18 - - 36/50 58/105 - - Orb View-3 1 4 4 4 4 - - 8 8 16 <3 Quick Bird-1 1 4 4 4 4 - - 22 22 20 1-5 IKONOS-2 0.8 3.3 3.3 3.3 3.3 - - 11 11 14 1-3 * Priroda Mission. Data for pan refer to the nadir panchromatic channel only. Table 1. Characteristics of selected spacebome sensors (modified after Fritz, 1996 ; Aplin et al., 1997).

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