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 4 FUSION OF SENSOR-DERIVED PRODUCTS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: ANISOTROPY INFORMATION FROM MOMS-02/PRIRODA STEREO DATASETS - AN ADDITIONAL PHYSICAL PARAMETER FOR LAND SURFACE CHARACTERISATION. Th. Schneider, I. Manakos, Peter Reinartz, R. Müller

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 103 oblique looking bands. Furthermore, the urban/man-made construction class often appeared overestimated in the combined approach, which, in relation to the failure of the anisotropy approach to distinguish accurately the bare soil signature from the urban area signature, could lead to the conclusion that this phenomenon is a result of the synergy effect between multispectral and anisotropic information. Among the classes that were common in all three approaches, the forest types could be well recognised in all of them. An information that is missing in the anisotropy approach is the presence of grass at the forest edges, which is presented accurately by the other two approaches. The class "peas" was more accurately estimated by the MS and the combined approaches. Slight differences between the MS and the combined approaches could be noticed through a detailed examination of the whole classified scene. These differences concern the capability of the maximum likelihood classifier to classify all pixels of the same plot to the same class (cultivation). In this respect, better performance was observed by the combined approach. The overall accuracy of the combined and the MS approach classification was more or less at the same level, with the combined approach being slightly better. The accuracy of the anisotropy approach was lower than the other two, which was expected, since the other two approaches, apart from bands 1 and 4, use part or most of the information that the anisotropy approach uses. 5. DISCUSSION Due to time constraints, the investigations on the Diimast dataset could not be performed with the completeness and accuracy, which are requested for reliable conclusions. The rectification was performed without DEM. No atmospheric and terrain relief corrections were applied to the dataset. Additionally, the evaluation of the potential of the anisotropy approach was limited due to the unfavourable illumination-to- sensor geometry. In spite of these facts, the false colour composite of the stereo data (6/7, 6, 7) showed a surprisingly high information content. In any case, the results allow discussing some trends. The "anisot" FCC displays texture and structure elements more clearly, improving the detection of infrastructure and settlements. The detailed analysis showed partially different features in the two FCCs. The example of winter wheat indicates that during specific stages of crop development, stand structure and/or individual plant architecture are more universal object characteristics than the multispectral ones. The example of mowed meadows demonstrates a situation, well-known from the side-looking perspective of a walker, which is also valid for remote sensing data analysis, i.e. that a plot looks green despite a ground coverage of almost 5%. With the anisotropy approach, it seems that is possible to detect sparse vegetation in a very early development stage. A point that should be noticed in the anisotropy approach is the hot spot and the shadow effect. At object boundaries where also significant height differences are observed, a buffer zone appears, where pixels have a distinct radiometric behaviour, as a consequence of the particularity of the irradiation at these regions. The result is that these pixels are often misclassified. In this case, they were often classified as urban/man-made construction class. An analysis of the results of the multispectral versus the anisotropic evaluation, both the visual as well as the computer- based one, indicates that the classification is determined by different biophysical parameters. In case of vegetation, the multispectral approach explores mainly the absorption of incoming radiation by pigments and water, while the anisotropy information is due to stand structure and plant architecture, which are often an effect of phenologic and physiologic status. The hypothesis that multispectral and anisotropic information are complementary could be proven at least for the main landcover classes forest, agricultural areas, settlements, water bodies and infrastructure. The synergistic potential of the combined use of multispectral and anisotropic information could not be demonstrated as clearly as in the investigations described by Schneider et al. (1999). For the evaluated dataset, the increase in classification accuracy for the combined multispectral and stereo band analysis is not significant. The low difference between the backscatter signal of the two stereo bands should be the reason. Despite this result of the computer-based analysis, the results of the visual interpretation let us hope that a further substantial increase can be expected using common classification routines, which consider also textural and context information like the DELPHI2™ approach (deKok et al., 1999). For more detailed investigations on this topic, field measurements, approximating the BRDF, have to be performed, preferably simultaneously to stereo data acquisition from space or airborne sensors. Last but not least, it is worth noting, that the mode D band combination of MOMS-2P has been proven to lead to better results than the mode 3 data of the D2 mission. The concept of optimised spectral coverage in the visible range by substituting band 3 (red) by band 1 (blue) led to a real improvement, especially for visual interpretation. By combining the multispectral bands 1 and 4 with the panchromatic stereo bands, which provide the spectral information from blue to red, almost true colour images can be produced.

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