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 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION. Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin

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 Edges are discontinuities in the gray levels of an image. Except for noise or systematic sensor errors, edges are caused by events in the object space. Examples of such events include physical boundaries of objects, shadows, and variations in the reflectance of material. It follows that edges are useful features, as they often convey information about objects in one way or another. Segmentation is another useful step in extracting information about objects. Segmentation entails grouping pixels that share similar characteristics. Unfortunately, this is a quite vague definition and not surprisingly often defined by the application. The output of the first stage is already a bit more abstract than the sensory input data. We see a transition from signals to symbols, however primitive they may still be. These primitive symbols are now subject of a grouping process that attempts to perceptually organize them. Organization is one of the first steps in perception. The goal of grouping is to find and combine those symbols that relate to the same object. Again, the governing grouping principles may be application dependent. The next step in model-based object recognition consists of comparing the extracted and grouped features (data model) with a model of the real object (object model), a process called matching. If there is sufficient agreement, then the data model is labeled with the object and undergoes a validation procedure. Crucial in the matching step is the object model and the representation compatibility between the data and object model. It is fruitless to describe an object by properties that cannot be extracted from the sensor data. Take color, for example, and the case of a roof. If only monochromatic imagery is available then we cannot use ‘red’ in the roof description. The sequential way on how the paradigm is presented is often called bottom-up or data driven. A model driven or top-down approach follows the opposite direction. Here, domain specific knowledge would trigger expectations, where objects may occur in the data. In practice, both approaches are combined. 2.2. Multisensor fusion Multisensor integration means the synergistic use of the information provided by multiple sensory devices to assist the accomplishment of a task by a system. The literature on multisensor integration in computer vision and machine intelligence is substantial. For an extensive review, we refer the interested reader to Abidi and Gonzalez (1992), or Hall (1992). At the heart of multisensor integration lies multisensor fusion. Multisensor fusion refers to any stage of the integration process where information from different sensors is combined (fused) into one representation form. Hence, multisensor fusion can take place at the signal, pixel, feature, or symbol level of representation. Most sensors typically used in practice provide data that can be fused at one or more of these levels. Signal-level fusion refers to the combination of signals from different sensors with the objective of providing a new signal that is usually of the same form but of better quality. In pixel- level fusion, a new image is formed through the combination of multiple images to increase the information content associated with each pixel. Feature-level fusion helps making feature extraction more robust and creating composite features from different signals and images. Symbol-level fusion allows the information from multiple sensors to be used together at the highest level of abstraction. Like in object recognition, identity fusion begins with the preprocessing of the raw sensory data, followed by feature extraction. Having extracted the features or feature vectors, identity declaration is performed by statistical pattern recognition techniques, or geometric models. The identity declarations must be partitioned into groups that represent observations belonging to the same observed entity. This partitioning - known as association - is analogous to the process of matching data models with object models in model based object recognition. Finally, identity fusion algorithms, such as feature-based inference techniques, cognitive-based models, or physical modeling are used to obtain a joint declaration of identity. Alternatively, fusion can occur at the raw data level or at the feature level. Examples for the different fusion types include pixel labeling from raw data vectors (fusion at data or pixel level), segmenting surfaces from fused edges extracted from aerial imagery and combined with laser measurements (feature level fusion), and recognizing buildings by using ‘building candidate’ objects from different sensory data (decision level fusion). Pixel level fusion is only recommended for images with similar exterior orientation, similar spatial, spectral and temporal resolution, and capturing the same or similar physical phenomena. Often, these requirements are not satisfied. Such is the case when images record information from very different regions of the EM spectrum (e.g., visible and thermal), or if they were collected from different platforms, or else have significantly different sensor geometry and associated error models. In these instances, preference should be given to the individual segmentation of images, with feature or decision level fusion. Yet another consideration for fusion is related to the physical phenomena in object space. Depending on the level of grouping, extracted features convey information that can be related to physical phenomena in the object space. Obviously, features extracted from different sensors should be fused when they have been caused by the same physical property. Generally, the further the spectral bands are apart, the lesser the features extracted from them are caused by the same physical phenomena. On the other hand, as the level of abstraction increases, more and

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