Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

Benz, U.; Hofmann, Peter; Willhauck, G.; Lingenfelder, Iris; Heynen, Markus

doi:10.1016/j.isprsjprs.2003.10.002

Cited by 2,186 publications

(1,456 citation statements)

References 18 publications

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“…A major advantage of classifying objects rather than pixels is the fact that features such as topology (i.e. relations to the neighboring objects), shape, texture, and a wider range of statistics on spectral signatures can be used to characterize the objects [9]. In all, 49 features were used to classify the image.…”

Section: Land-cover Mapmentioning

confidence: 99%

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

et al. 2007

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-The recent and rapid spread in the Mediterranean Basin of bluetongue, a viral disease of ruminants transmitted by some species of Culicoides (biting midges), highlights the necessity of determining the conditions of its emergence. This study uses high spatial resolution satellite imagery and methods from landscape ecology science to identify environmental parameters related to bluetongue occurrence in Corsica, a French Mediterranean island where the disease occurred for the first time in 2000. A set of environmental variables recorded in the neighborhood of 80 sheep farms were related to case occurrence through a logistic regression model computed within three subsequent buffer distances of 0.5, 1 and 2 km. The results reveal the role of landscape metrics, particularly those characterizing land-use units such as prairies and woodlands, as well as farm type, latitude and sunshine to explain the presence of bluetongue. Internal and external validation both indicate that the best results are obtained with the 1 km buffer size model (area under Receiver Operating Characteristic curve = 0.9 for internal validation and 0.81 for external validation). The results show that high spatial resolution remote sensing (i.e. 10 m pixels) and landscape ecology approaches contribute to improving the understanding of bluetongue epidemiology.epidemiology / bluetongue / remote sensing / geographic information systems / landscape ecology

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Section: Land-cover Mapmentioning

confidence: 99%

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

et al. 2007

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“…The algorithm initializes each pixel in the image as a separate segment, which is consecutively merged based on level of similarity. The maximum heterogeneity allowed within an image object was defined by a parameter called scale (Benz et al, 2004). The users can also specify two other parameters, shape and compactness, to define the relative weight of reflectance and shape in segmentation.…”

Section: Object-based Classificationmentioning

confidence: 99%

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

Yan

Lin

Zhou

et al. 2016

Journal of Environmental Sciences

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It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope (SEM) micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found (1) Rosa xanthina Lindl (shrub species) had greater retention efficiency than Broussonetia papyrifera (broadleaf species) and Pinus bungeana Zucc.(coniferous species), in terms of particle number and particle area cover. (2) 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical. (3) 32.4%-74.1% of the particles were generated from bare soil and construction activities, and 15.5%-23.0% were mainly from vehicle exhaust and cooking fumes.

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“…Morphological, relational, and spatial properties were analyzed to distinguish between several urban areas with buildings from different decades. Bauer and Steinnocher (2001) used XRAG implemented in SAMS (Structural Analyzing and Mapping System) as a prerequisite for the definition of rules within eCognition software (Benz et al, 2004) to derive LU on a rule based method. De Almeida, Morley, and Dowman (2007) describe a graph based approach to analyze topological structures of urban regions.…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Urban Land Use Mapping From High Resolution Satellite Images

Walde

Hese

Berger

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Due to the dynamic character of urban land use (e.g. urban sprawl) there is a demand for frequent updates for monitoring, modeling, and controlling purposes. Urban land use is an added value that can be indirectly derived with the help of various properties of land cover classes that describe a certain area and create a distinguishable structure. The goal of this project is to extract land use (LU) classes out of a structure of land cover (LC) classes from high resolution Quickbird data and additional LiDAR building height models. The study area is Rostock, a German city with more than 200.000 inhabitants. To model the properties of urban land use a graph based approach is adapted from other disciplines (industrial image processing, medicine, informatics). A graph consists of nodes and edges while nodes describe the land cover and edges define the relationship of neighboring objects. To calculate the adjacency that describes which nodes are combined with an edge several distance ranges and building height properties are tested. Furthermore the information value of planar versus non-planar graph types is analyzed. After creating the graphs specific indices are computed that evaluate how compact or connected the graphs are. In this work several graph indices are explained and applied to training areas. Results show that the distance of buildings and building height are reliable indicators for LU-categories. The separability of LU-classes improves when properties of land cover classes and graph indices are combined to a LU-signature. KURZFASSUNG:Aufgrund des dynamischen Charakters von Städten (Suburbanisierung/Zersiedelung) entsteht ein Bedarf an regelmäßiger Aktualisierung urbaner Landnutzungsdaten für Modellierungs-,Überwachungs-und Kontrollzwecke. Urbane Landnutzung stellt einen Mehrwert dar, der mit Hilfe von spezifischen Eigenschaften von Landbedeckungsklassen, die ein bestimmtes Gebiet beschreiben und eine erkennbare Struktur bilden, indirekt abgeleitet werden kann. Das Ziel des Projektes ist die Extraktion städtischer Landnutzungstypen aus Landbedeckungsklassen, die aus hochaufgelösten optischen Quickbird-Satellitendaten und einem LiDAR-Oberflächenmodell abgeleitet wurden. Das Untersuchungsgebiet Rostock ist eine deutsche Stadt mit mehr als 200.000 Einwohnern. Um die Eigenschaften der Stadtstrukturen zu modellieren wurde ein graphenbasierter Ansatz, der bereits in anderen Wissenschaftszweigen (industrielle Bildverarbeitung, Medizin, Informatik) eingesetzt wird, adaptiert. Ein Graph besteht aus Knoten, die die Landbedeckung repräsentieren, und Kanten, die die Eigenschaften zu benachbarten Objekten definieren. Um die Adjazenzmatrix zu berechnen, die beschreibt welche Knoten und Kanten miteinander verbunden sind, wurden verschiedene Distanzbereiche und Höhenangaben getestet. Darüber hinaus wurde der Informationswert planarer und nicht-planarer Graphen analysiert. Nach der Graphenbildung wurden spezifische Indikatoren berechnet, die beschreiben wie kompakt oder zusammenhängend der Graph ist. In diesem P...

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Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

Cited by 2,186 publications

References 18 publications

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

Graph-Based Urban Land Use Mapping From High Resolution Satellite Images

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