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 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: ADAPTIVE FUSION OF MULTISOURCE RASTER DATA APPLYING FILTER TECHNIQUES. K. Steinnocher

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 114 (a) (b) Fig. 5. Overlay of vectorised forest masks to aerial reference image, © BEV 1992. (a) Forest mask derived from Landsat TM image, (b) Forest mask derived from AIF image, © Joanneum Research 1999. between the fusion results and the “true” image could be performed. The evaluation proved the spectral stability of the algorithm and the higher correlation between the “true” and the fused image compared to the degraded image. The third case study showed the application of AIF to multisensor image data acquired by Landsat TM and SPOT PAN. The objective was the derivation of a forest mask using a threshold technique. While the thresholding of the Landsat TM bands provided a reliable mask, the shape of this mask could be significantly improved by first applying the AIF to the multispectral bands and the panchromatic image. The conclusions drawn from the application in the test sites are manifold. The AIF is considered useful for areas that are dominated by objects larger than the low resolution pixel size. Taking the average pixel size of current multispectral sensors this prerequisite is only true for certain types of image objects, such as agricultural fields. However, with the increasing spatial resolution of the new sensor generation, this limitation will become less and less important. On the contrary, the application of AIF might help to reduce the enormous complexity of very high resolution images. In cases of small objects that do not benefit from the AIF, we suggest to use the fusion result for first level classification, i.e. classification of meta-classes that provide image masks for subsequent detailed classification. The advantage of this approach lies in the sharper delineation of the meta-objects, thus leading to less misclassifications caused by mixed pixels. A final suggestion refers to the combination of AIF and substitution techniques for visualisation. AIF would first sharpen object edges and thus eliminate the blocky pixel structure of the low resolution image. Then the local texture could be added by applying a substitution technique onto the AIF processed image, thus leading to a sharpened high resolution multispectral image product. However, this product is not considered an appropriate input for numerical classification but offers an improved basis for visual interpretation of the image. ACKNOWLEDGEMENTS Part of this work has been supported by the European Commission, Contract No. ENV4-CT96-0359. The author thanks W. Pillmann (OBIG) for providing the ATM data of Vienna. The contribution of Ursula Schmitt (Joanneum Research) to the forest case study is greatfully acknowledged. REFERENCES Benediktsson J.A. and Swain P.H. 1992. Consensus theoretic classification methods. IEEE Trans. Syst., Man, Cybem., 22(4), pp. 688-704. Carlson G.R. and Patel B. (1997): A new area dawns for geo spatial imagery. GIS World, 10(3), pp. 36-40. Carper W.J., Lillesand T.M. and Kiefer R.W., 1990. The use of intensity-hue-saturation transformation for merging SPOT panchromatic and multispectral image data. Photogrammetric Eng. Remote Sensing, 56(4), pp. 459-467. Chavez P.S., Sides S.C. and Anderson J.A., 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Eng. Remote Sensing, 57(3), pp. 295-303. Janssen L.L., Jaarsma M.N. and van der Linden E.T., 1990. Integrating topographic data with remote sensing for land-cover classification. Photogrammetric Eng. Remote Sensing, 56(11), pp. 1503-1506. Garguet-Duport B., Girel J., Chassery J.-M., Pautou G., 1996. The use of multiresolution analysis and wavelet transform for merging SPOT panchromatic and multispectral image data. Photogrammetric Eng. Remote Sensing, 62(9), pp. 1057-1066. Harris J.R., Murray R. and Hirose T., 1990. IHS Transform for the integration of radar imagery with other remotely sensed data. Photogrammetric Eng. Remote Sensing, 56(12), pp. 1631-1641.

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