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:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: CLASSIFICATION OF SETTLEMENT STRUCTURES USING MORPHOLOGICAL AND SPECTRAL FEATURES IN FUSED HIGH RESOLUTION SATELLITE IMAGES (IRS-1C). Maik Netzband, Gotthard Meinel, Regin Lippold

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 160 CLASSIFICATION OF SETTLEMENT STRUCTURES USING MORPHOLOGICAL AND SPECTRAL FEATURES IN FUSED HIGH RESOLUTION SATELLITE IMAGES (IRS-1C) Maik Netzband, Gotthard Meinel, Regin Lippold Institute for Ecological and Regional Development Dresden, Weberplatz 1, D-01217 Dresden, e-mail: maik.netzband@pop3.tu-dresden.de KEYWORDS: Image Classification, Human Settlement, High Resolution Satellite Imagery, IRS-1C, Data Merging, Urban and Regional Development Monitoring. ABSTRACT The development of a hierarchical classification scheme is presented which combines spectral and morphological analysis methods. For an 1RS-1C merge product, a multispectral classification is performed to separate large-area landuse classes, mostly in the urban fringe, e.g. water, forest and agricultural areas, excavation sites, etc. Areas covered by vegetation can be separated in an exact manner using the vegetation index NDVI (ratio of red and near infrared channels). Concerning pure settlement areas, a combination of filter techniques (top-hat and bottom-hat transformations) followed by a thresholding (hysteresis-threshold) in order to identify settlement structures is presented. Furthermore, a classification of urban structure types (mainly residential areas) by using their morphological and textural characteristics is developed. For both the precise identification of urban (settlement) areas, as well as the urban structure- type classification, there is an urgent demand by spatial planning authorities (urban and regional planning). 1. INTRODUCTION Spatial planning requires information on landuse status in ever shorter time intervals and higher spatial resolution. This demand can only be met by using new high resolution satellite data. The Indian IRS-1C is currently delivering data with a 5- metre ground resolution; other sensors (IKONOS-2, Orbview 3) scheduled for 1999 will provide data in the 1-metre resolution range. The project assesses the suitability of IRS-1C data for diverse planning requirements, e.g. updating of landuse plans, municipal survey maps, maps of urban structure types and biotopes, surface-sealing surveys, and working maps for landscape planning. It also examines the potential of IRS-1C data for providing the basis for general data updating at a scale of 1 : 25,000. This paper focuses on the use of IRS-1C data for automatic classification in urbanized areas and identification of settlement structures by combining spectral with morphological information contained in the panchromatic data. Multispectral pixel-based classification is basically possible with the 3-band IRS-1C LISS data but is of limited use in the very heterogeneous urban areas with high frequent changes of landuse. In the past, there were no automatic and operational methods available for urban settlement detection, but only adaptations from other application fields (like forest or agricultural monitoring). In addition, no attention was given to the morphology and spatial pattern of the detected objects/surfaces in conventional classification methods, although the spatial pattern is a key dimension of human settlement analysis and planning. For these reasons, it is necessary to develop new methods and concepts involving morphological and textural analysis. Visual interpretation of morphological grey value distributions in high resolution, panchromatic data leads to a differentiation of various objects and structures. In this process, the human brain analyses many different features, e.g. shape, texture, location and spatial interrelations of image grey values. Automatic calculation of these parameters is hard to reach. Therefore, it is necessary to develop innovative techniques or use existing tools for analysing the morphology of the grey value distribution of panchromatic images and integrate them in hierarchical classification schemes combining the spectral and the morphological information of panchromatic images. 2. STATE OF THE ART Due to the improved geometric resolution of new satellite-based sensors, the analysis of built-up or more general of settlement areas (depending on the different monitoring scales and on task definition, e.g. the analysis can involve the identification of single objects or whole settlement areas of towns and villages) is faced with new challenges, which can not be solved only by utilizing conventional multispectral classification. For this reason, several current research efforts - from different points of view - try to develop new strategies for very heterogeneous and rapidly changing urban and suburban areas. From the photogrammetric point of view, an overview of the present research activities to identify man-made objects mainly in airborne data is given by Grim et al. (1997). Fewer efforts are undertaken up till now to investigate the potential use of new high resolution satellite data. Presently, only IRS-1C is delivering high resolution data (5,8 m in the panchromatic modus). With this resolution, it is not possible to detect single objects (buildings, roads, etc.), large size buildings excluded. On the other hand, it has to be verified whether the IRS-1C data are suitable for outlining connected settlement areas, i.e. for

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation