By H. G. Coskun (auth.), H. Gonca Coskun, H. Kerem Cigizoglu, M. Derya Maktav (eds.)
In case of safeguard threats there's a desire for temporal entry to accomplish, up-to-date, trustworthy details, in a devoted shape, is a vital prerequisite to successfully counter safety threats. at the moment the mandatory info exists in a large number of varieties and codecs geographically and bodily scattered over various international locations, institutes and enterprises, and are topic to generally various information guidelines and administration schemes. furthermore, there's no whole and up-to-date evaluate of the prevailing info, e.g. within the type of a metadata catalogue. hence, integration of this data in case of emergencies has confirmed to be super tough, if now not most unlikely. even if every so often, fascinating and bold "demonstrations" were proven of the probabilities of the mixing of knowledge, for a few purposes those have reached the "operational" stage.
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32 M. EHLERS Figure 13. Binary mask for the SPOT 5 study site (settlement in red). Figure 14. Binary mask for the KOMPSAT 1 study site (settlement in red). GEOSPATIAL INTEGRATION AND FUSION TECHNIQUES 33 Despite the differences between the used datasets, the results are very similar (see Figures 13 and 14). Contiguous settlement areas (conurbation areas) are detected with a high accuracy. For both test areas the borders between “settlement” (red) and “nonsettlement” (no color) are represented with a low level of generalization (borders are not smooth).
The cookie cutter approach improved the classification accuracy by nearly 20%. , the differentiation of surfaces with similar features but different materials) could be realized. Figure 8. Overall classification accuracy for the RGB data alone (left), RGB + DSM data (center) and RGB + DSM + SAM scores (right). 4. Decision-based Fusion As a basis for the decision-based fusion process, we selected high- and mediumspatial resolution satellites data to develop, implement and test a method for the automated detection of settlement areas.
Mistakes and erroneous thresholds can be interactively corrected. Once the selected NDVI classes have been verified by visual analysis, the image is separated into independent layers and processed similar to the previous process based on the GIS input. GEOSPATIAL INTEGRATION AND FUSION TECHNIQUES 41 Figure 21. NDVI for the Quickbird image of the study site. Bright values indicate high level of vegetation, intermediate values open fields and dark values water. Figure 22. Histogram of the NDVI image.