A framework for GIS and imagery data fusion in support of cartographic updating

Weis, Martin; Müller, Sönke; Liedtke, Claus-E.; Pahl, M.

doi:10.1016/j.inffus.2004.08.001

Cited by 18 publications

(10 citation statements)

References 4 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous research has shown that the fusion of remote sensing and GIS has played a critical role in map updating (Weis et al 2005, Yang and Zhang 2005, Yang et al 2008, Aydo¨ner and Maktav 2009. The integration of remote sensing and GIS is emerging as a new research field.…”

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

“…Therefore, the integration of data from different applications has to concern the differences in the object model and semantics of the objects themselves (Fonseca et al 2002, Weis et al 2005. Images are usually composed of a raster of pixels representing the intensities in the RGB-domain; whereas GIS data contains artificial objects with label forms, representing the objects or region affiliations.…”

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

“…Therefore, in the latter case, the two sources of data must be unified into object level and fused. Due to the differences of characteristic levels of information and semantics between the two different data sources, the comparison of image data and GIS data cannot take place on the pixel level, but has to be accomplished on a level where aggregation of low-level image objects to meaningful objects has taken place (Weis et al 2005). The object-oriented classification and change detection approach provides an opportunity to integrate these two different data sources in a seamless way since an inherent characteristic of this method is the aggregation of the image pixels to semantically meaningful polygon objects and thus can be overlaid and analysed with vector format GIS data.…”

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

“…One of the main challenges concerns the knowledge modelling (Fabre and Dherete 2003). Valuable work has been accomplished in this field; in cartographic updating, Weis et al (2005) made use of a knowledge-based approach to model the objects that are expected to be found and implemented the combination of GIS data and aerial images. The results show that an automatic interpretation can be achieved using GIS information.…”

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

See 3 more Smart Citations

Multi-source remote sensing data fusion: status and trends

Zhang

2010

International Journal of Image and Data Fusion

585

325

View full text Add to dashboard Cite

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.

show abstract

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

Section: Fusion Of Optical Images and Gis Datamentioning

confidence: 99%

See 2 more Smart Citations

Multi-source remote sensing data fusion: status and trends

Zhang

2010

International Journal of Image and Data Fusion

585

325

View full text Add to dashboard Cite

show abstract

“…National Geographic Institute 1/10000 topographic maps) were carried out by digital cartographic production process based on aerial photographs, and the update of these databases is now at the agenda. Until now, interpretation of aerial photographs is a standard tool for monitoring land cover change where fine spatial resolutions are required (Loveland et al, 2002, Weis et al, 2005 and this task is expensive and time-consuming. Though, from a spaceborne perspective, the VHR satellite data are, since 1999, capable to meet the mapping and monitoring needs of municipal and regional planning agencies (Treitz P., J. Rogan, 2004).…”

Section: Introductionmentioning

confidence: 99%

Change detection for updates of vector database through region-based classification of VHR satellite data

Carleer

Wolff

2007

SPIE Proceedings

View full text Add to dashboard Cite

ABSTRACT:Until now, interpretation of aerial photographs is a standard tool for monitoring land cover change where fine spatial resolutions are required and this task is expensive and time-consuming. Though, from a spaceborne perspective, the VHR satellite data are, since 1999, capable to meet the mapping and monitoring needs of municipal and regional planning agencies. Indeed, these data from the sensors Ikonos, QuickBird, OrbView-3, and in near future, the Pléiades-HR French sensors, have spatial resolution lower than 5 m in multispectral mode and lower than 1 m in panchromatic mode. These new sources of data combine the advantages of satellite data (synoptic view, digital format suitable for computer processing, quantitative land surface information at large spatial coverage and at frequent temporal intervals …) with the very high spatial resolution. In spite of these advantages, the use of VHR satellite data involves some problems in traditional per-pixel classification often used in change detection techniques. There are still two occurring classification problems that can strongly deteriorate the result of a perpixel classification of the VHR satellite data: spectral variability and poor spectral resolution. A solution to overcome these problems is the region-based classification that can be integrated in the common change detection techniques. The segmentation, before classification, produces regions which are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each image region then becomes a unit analysis and makes it possible to avoid much of the structural clutter. Image segmentation provides a logical transition from the units of pixels to larger units in maps more relevant to detect the changes in these. In this context, this research project suggests to use region based classification of VHR satellite data in the change detection processe for updates of vector database.

show abstract

Information Retrieval in the Geodetic and Cartographic Documentation Centers

Kubik

Iwaniak

2009

Studies in Computational Intelligence

View full text Add to dashboard Cite

A framework for GIS and imagery data fusion in support of cartographic updating

Cited by 18 publications

References 4 publications

Multi-source remote sensing data fusion: status and trends

Multi-source remote sensing data fusion: status and trends

Change detection for updates of vector database through region-based classification of VHR satellite data

Information Retrieval in the Geodetic and Cartographic Documentation Centers

Contact Info

Product

Resources

About