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 more different phenomena are described and need to be explained. 2.3. Dataset To illustrate the main steps of the proposed approach, we use a dataset collected over Ocean City, Maryland on April 25 and 30, 1997 (http://polestar.mps.ohio- state.edu/~csatho/wg35.htm). Ocean City is located along a narrow peninsula by sandy beaches on the east, and harbors and docks on the west coast. High-rise buildings on the east and residential areas on the west side flank the main road. The dataset comprises of aerial photography, multispectral scanner imagery, and scanning laser data. As a part of the laser altimetry data, precise GPS positions and INS attitude have also been recorded. Csatho et al. (1998) describe the dataset in more detail. Digital elevation data were acquired by the Airborne Topographic Mapper (ATM) laser system. The ATM is a conical scanning laser altimeter developed by NASA to measure the surface elevation of ice sheets and other natural surfaces, such as beaches, with ca. 10 cm accuracy (Krabill et al., 1995). The multispectral data were collected by the Daedalus AADS-1260 airborne multispectral scanner from the National Geodetic Survey (NGS). The AADS-1260 is a multispectral line scanner with eleven spectral bands in the visible, near infrared and thermal infrared providing 1.8-2.5 m pixels on the ground. Aerial photography was acquired with an RC20 camera, also from NGS. The laser scanner and the multispectral scanner were mounted on NASA's P-3B aircraft. The aerial camera was operated independently by NGS, but on the same day. The original data have been preprocessed. For example, the GPS, INS, and laser range data were converted into surface elevations of laser footprints. The aerial photographs were scanned with a pixel size of 28 microns and an aerial triangulation with control points established by GPS provided the exterior orientation parameters. However, due to the lack of instrument calibration data, we could not transform the multispectral dataset into ground reflectance to establish a match with library spectra. 3. PROPOSED SYSTEM FOR MULTSENSOR OBJECT RECOGNITION Figure 1 depicts a schematic diagram of a data fusion architecture that is tailored for combining aerial and multispectral imagery, and laser scanning data for object recognition in urban scenes. It includes modules that are considered important in model based object recognition. The overall goal is to extract and group those features from the sensory input data that lead to a rich data model. This, in turn, permits modeling the objects with additional attributes and relationships so that the differences between data and object model become smaller, resulting in a more stable and robust recognition. aerial laser multispectral K J I H GFEDCBA Fig. 1. Combination of sensor fusion and object recognition. A common approach to fuse multispectral imagery and laser scanning data is to convert the latter into a range image, to add it as a separate channel to the multispectral data and to classify this combined set of data. This elegant approach is based on the assumption that the classes correspond to objects. Our approach is radically different, as we are quite skeptical on whether object recognition can be satisfactorily solved by classification. Rather, we advocate extracting features from the individual sensors and fuse them at appropriate levels, following the general rules described in the previous section. It has long been realized that surfaces play an important role in object recognition. However, to be useful, surface information must be explicit and suitably represented. This entails extracting surface features from laser scanning data and aerial imagery followed by fusing them, leading to the

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