The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Chen, Jun

Bibliographic data

Monograph

Persistent identifier:: 856566209

Author:: Chen, Jun

Title:: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Sub title:: May 23 - 25, 2001, Bangkok, Thailand

Scope:: VI, 434 Seiten

Year of publication:: 2001

Place of publication:: Pathumthani, Thailand

Publisher of the original:: AIT

Identifier (digital):: 856566209

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:: PER-FIELD CLASSIFICATION INTEGRATING VERY FINE SPATIAL RESOLUTION SATELLITE IMAGERY WITH TOPOGRAPHIC DATA. Mauro CAPRIOLI, Eufemia TARANTINO

Document type:: Monograph

Structure type:: Chapter

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 ISPRS, Vol.3- 32 PER-FIELD CLASSIFICATION INTEGRATING VERY FINE SPATIAL RESOLUTION SATELLITE IMAGERY WITH TOPOGRAPHIC DATA. Mauro CAPRIOLI, Eufemia TARANTINO POLYTECHNIC UNIVERSITY OF BARI Dept, of Highway and Transportation Via Orabona 4, 70125 BARI - ITALY Tel.: +39 080 5569387 - Fax: +39 080 5569329 E-mail: caprioli@dvt005.poliba.it; tarantino@dvt005.poliba.it Keywords: Satellite Imagery - Topographic Data - GIS - Per-field Classification ABSTRACT The paper analyses an automated system for classifying land use on a locale scale, integrating Remote Sensing and GIS data, with the aim of extracting meaningful information in performing spatial analysis in urban contexts. As a first step, the backgrounds in extraction of contextual information from fine spatial resolution satellite imagery in an integrated RS/GIS system are considered in detail. Finally, a per-field classification technique, applied to an IKONOS multispectral image with spatial resolution of 4 m by utilising a digital topographic map, is described and the accuracy assessment of classification with the nature of problems emerged in the procedure is clearly identified. 1. INTRODUCTION The information contained in digital imagery, acquired by Remote Sensing technology, can be used for mapping, monitoring and assessing the properties of the environmental and territorial feature elements. Of all these three main application fields in Remote Sensing, making thematic cartography by means of automatic classification methods is surely among the most widespread and in many cases is an essential preliminary step for further applications. Up to now spatial resolution of data given by earth observation satellites has proved inadequate in providing detailed topographic peculiarities, in specific application domains of analysis and monitoring of urban environment. Modality of terrestrial phenomenon representations, augmented geometric accuracy, temporal flexibility of acquiring, spatial land cover and appropriate use in spatial modelling terms suggest, instead, the diffusion and continual use of new very fine spatial resolution satellite sensors imagery, as data sources for spatial analysis in urban contexts. The high level of investigations enabled by interacting with other disciplinary sectors, can support planners’ activities better, as Mesev, Longley and Batty (1996) have argued: “our concern with land use revolves around the central 'urban/non urban' dichotomy as manifest through physical form, although in practice finer disaggregations as well as measures of the intensity of human activities are also desirable". The informational classes of a thematic mapping are not directly registered, but must be derived indirectly by using evidence contained in the spectral data of an image. When we apply standard procedures of per-pixel multispectral classification the increase of spatial resolution leads to augmentation in ambiguity in the statistical definition of land cover classes and a decrease of accuracy in automatic identification. This problem may be overcome by means of per-field classification techniques which involve analysing groups of pixels within land cover parcels. Such technique, based on the integration of remotely sensed imagery and digital vector data, has been used to generate land cover and land use information for more than a decade (Carbone, Narumalani and King, 1996; Ehlers, Greenlee, Smith and Star, 1991; Hinton, 1997;). Innovation and power of recent GIS platforms and analytic flexibility of Image Processing softwares make the integration of satellite data with numerical and scaled topographic data much more feasible, and this can lead to an increase in accuracy of the classification compared with the per-pixel technique, as well as improvements in interpretations of results with incorporating spatial variability and texture inherent in fine spatial resolution imagery. This work investigates a per-field classification methodology, applied to an IKONOS multispectral image with spatial resolution of 4 m by utilising a digital large scale topographic map as a representative reference land cover, and assesses the accuracy of classification by comparing the results carried out for both the per - pixel and the per - field techniques. Finally, the nature of problems emerged with both procedures is clearly identified. 2. THE EXTRACTION OF CONTEXTUAL INFORMATION FROM FINE SPATIAL RESOLUTIONSATELLITE IMAGERY IN AN INTEGRATED RS/GIS SYSTEM. The accuracy with which land use has been mapped up to now from satellite sensor imagery from local to national scales has been limited by the relatively coarse spatial resolution of instruments. For example, for the land cover maps generated using Landsat Thematic Mapper (TM) imagery, with a spatial resolution of 30 m, a considerable amount of detail in the scene is obscured from the image. The availability of recent multispectral imagery with very fine spatial resolution has increased our ability to map land use in geometric detail and accuracy Aplin, Atkinson and Curran, 1997) for local and national scale investigations. However, these sources of imagery are likely to generate other problems. Even if the radiometric resolution is enhanced (11 bit for IKONOS imagery), spectral capabilities are generally limited compared to those of the previous generation sensors (seven bands for the Landsat TM). Moreover, associated with an increase in spatial resolution there is, usually, an increase in variability within land parcels ('noise' in the image) generating a decrease in accuracy of land use classification on a per-pixel basis (Townshend, 1992). Traditional automated classification techniques classify land use on a basis of spectral distribution of the pixels within an image, whereby each pixel is associated with the most similar spectral class. This general method can produce results that are 'noisy' due to the high spatial frequency of the land covers. The alternative technique of per-field classification (so called because fields, as opposed to pixels, are classified as independent units) takes into account the spectral and spatial properties of the imagery, the size and shape of the fields and the land cover classes chosen. In fact, this approach requires a priori information about the boundaries of objects in the image, for examples, roads fields. If the boundaries of these fields are digitised and registered to the image, then some property of the pixel lying within boundaries of the field can be used to characterise that fields. For instance, the means and standard deviations in the four IKONOS bands of pixel lying within roads fields could be used as features defining the spectral reflectance properties of fields. Normally, the use of map and image data would take place within a geographical information system (GIS), which provides facilities for manipulating digitised boundary lines (for example, checking the set of line to eliminate duplicated boundaries, ensuring that lines 'snap on’ to nodes, and identifying illogical lines that end unexpectedly). All these ( spatial resc lead to an ii for relative Westmorelc However, tl with field b be carried c before during after cl Many exan two. Westr stage, cart< classificatio Alternative!; basis befor data for per The ways w integrated ir a) separa systerr b) two so shared c) a singl In this studv of the high and the pos spatial infori 3. TH This researi per 5 Km) v (Brindisi) in

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