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 113 Fig. 4. Fusion of ATM image data, (a) original image (6m), © ÔBIG 1991. (b) degraded image (18m). (c) AIF imaee f6ml. geocoded with respect to a digital terrain model in the Austrian reference system, thus no geometric distortions appear between the images. The derivation of the forest mask is based on thresholding of the single Landsat TM image bands. Applying this technique on the original image bands leads to a reliable forest mask, however the geometric resolution of this mask is limited by the pixel size of 30m. On the other hand, the panchromatic image that would provide the better spatial resolution does not offer the spectral information needed to separate the forest areas from other land cover objects. The fusion of both data sets was expected to result in an improved forest mask. The AIF was applied to the two data sets using a window size of 15 x 15, 7 iterations and a variance decreasing iteratively from 0.025 to 0.01. Next the thresholding technique was applied to the resulting multispectral fusion product using the same thresholds as for the original Landsat TM bands. As expected, the resulting forest mask delineates the forest areas significantly better. Fig. 5 shows the two vectorised forest masks superimposed to an aerial colour infrared photograph of the area. Visual evaluation of the results shows the improvement gained from the fusion approach. The borderlines of the forest mask are smoother and follow the actual border of the forest areas. Due to the pre segmentation effect of the AIF, only areas of a certain minimum crown coverage are recognized as forest. 4. SUMMARY AND CONCLUSIONS The case studies demonstrated the benefits and limitations of the AIF algorithm. The AIF was designed as a pre-processing tool for subsequent numerical classification. Its advantage lies in the pre-segmentation of the fused image, resulting in a low variance within the objects and sharp borders between the objects. The inclusion of highly textured areas from the panchromatic image is not supported by the technique, but the spectral characteristics of the multispectral image are preserved to a high extent. The first case study demonstrated the use of AIF for agricultural applications. Objects of interest are the single fields that represent the different agricultural crops of the area. These objects are expected to be of such a size that their spectral characteristics are represented even by the lower resolution of the multispectral image. Application of AIF improves a subsequent classification by sharpening the borders of the fields, thus reducing the number of mixed pixels and allowing for a more precise estimation of the actual areas covered by the single crop types. The urban case study proved the applicability of AIF to very high spatial resolution multispectral data. By degrading the multispectral image to a lower spatial resolution and fusing the result with an artificial panchromatic image, a comparison

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