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 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INTEGRATION OF IMAGE ANALYSIS AND GIS. Emmanuel Baltsavias, Michael Hahn,

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 In 14 Above applications are in 2D and increasingly in 3D, while multitemporal (4D) approaches are still rare. Quite long is the list of problems, which are encountered with respect to data and information fusion: • Differences between landuse (provided by GIS) and landcover (provided in images). • Lacking procedures for interpretation and quality control of fused images. • Fusion and the mixed pixels problem. • Distortion of spectral properties with pixel-based fusion techniques (partially avoidable by feature-based fusion). • Different levels of data quality regarding geometric accuracy and thematic detail. • Large differences in fusion of multitemporal data and change detection. • Differences between the data in spatial and spectral resolution (centre and width of band), as well as polarisation and angular view. • Data generalisation in map and GIS data. • Different levels of data abstraction and representation (resolution/scale). • Models of objects used in image analysis are often simplistic, limited in number and not general enough; on the other hand, generic models may be too weak and broad. • Different data representations, even for the same object or object class (roads, road networks). • Different data structures for the same object (e.g. raster, vector, attribute). • Lack of accuracy indicators for the components to be fused. • Data are often inhomogeneous, i.e. acquired by different methods, several analysts etc. • Algorithms to fuse information are restrictive, mechanical, not intelligent enough. • Abrupt decisions (as humans take sometimes) are not permitted by algorithms. Rules/models (e.g. roof ridges are horizontal) always have exceptions, but still should be used, e.g. with associated probabilities which can be updated by accumulation of knowledge, processed data etc. • Rules/models differ spatially (e.g. buildings in Europe differ from those in developing countries) and in time (old buildings differ from new ones). • Architecture of systems is complex, processing requirements high, commercial systems or support tools are limited or non existent. • Gap between research and practice (which is typical for not matured scientific areas). 3 3. USE OF GIS DATA AND MODELS IN IMAGE ANALYSIS GIS data and generally object models are generally used to provide information (geometrical, spectral, textural, functional, temporal etc.) about the target object(s), its attributes, as well as other objects and information related to the target ones. GIS information can be used in image analysis for various purposes: • Provision of initial approximations for some unknown parameters and thus reduction of the search space and increase of the probability of success. • Provision of clues for target objects based on information on other objects, related to the target ones, e.g. use of existing information on road network to detect buildings. • Quality control of the results, e.g. by serving as "ground truth". • In classification, e.g. in supervised classification to automatically select training areas, and in unsupervised one to automatically assign detected information classes to thematic classes. • Use of provided cues and information in various stages of object recongnition and reconstruction, e.g. to: (a) find objects, e.g. buildings by extracting the 3D blobs of a given DSM ; (b) exclude wrong hypotheses and detect blunders, (c) exclude regions that are impossible for a target object, e.g. building or road in water surfaces. • Use of data to support hypothesis generation about the model, e.g. try to infer the roof type (or some possible ones) based on the given building outline. In most cases, the integration of images analysis and GIS can not lead to full automation. Thereby, human interaction and intervention becomes necessary. The important points are how and when this interaction should occur. Generally, the interaction can (a) be preventive or have a guidance character, and (b) be corrective. Usually, the first case occurs at the beginning of the processing, the second one at the end. Whether the first or second approach is more appropriate depends on how much the quality and efficiency (time aspects) of the whole process are improved, as the result of such interaction. In this respect, preventive approaches seem to be preferable. Human intervention is also necessary to define the framework of the solution to a given problem: • Analysis of the problem, definition of the strategy. • Selection of building blocks that should be used (data, knowledge sources, processing methods etc.). • Decision on interactions between the blocks and definition of the processing flow. 4. PREREQUISITES FOR INFORMATION FUSION Before fusing and integrating information, several prerequisites should be fulfilled: Co-registration. By this we mean that the different components should be compatible/comparable with respect to various aspects: spatial (data should refer to the same area and coordinate system), temporal, spectral (inch appropriate corrections due to terrain relief, atmosphere, sensor calibration), resolution (pixel footprint, number of bits per pixel). Spatial co registration is always a prerequisite. An overview of co registration and geocoding methods is given in Raggam et al., 1999 (these methods should be appended by the increasingly used direct sensor data geocoding methods, using integrated GPS and INS). Depending on the application and the level of fusion, co-registration with respect to some of the above aspects is not necessary. E.g. while image fusion of multispectral and high resolution SAR imagery might be incompatible and not appropriate, integration of object cues from such images might be feasible and desirable. The same applies to temporal co registration, e.g. while for object extraction multiple data of the same o propert: change is not a operatic have ti transfer Same s direct c road ce: Clear o not, as user, an an exan part of exampli networl centerli two-diri bicycle intersec general] in some Need o data its Unfortu which i among transfer Quality combim often n< DTM ol for a cl or may success] needed, better appropr and fusi Regardi • The c will a be me • The d from often Instea detect comin locali: • Deep disad^ appro

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