1997 ◽  
Author(s):  
Randall D. Beer ◽  
Roger Quinn ◽  
Roy Ritzmann ◽  
Hillel Chiel

2014 ◽  
Vol 5 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Arpita Sharma ◽  
Samiksha Goel

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.


2002 ◽  
Vol 2 (1/2) ◽  
pp. 3-14 ◽  
Author(s):  
F. Ardizzone ◽  
M. Cardinali ◽  
A. Carrara ◽  
F. Guzzetti ◽  
P. Reichenbach

Abstract. Identification and mapping of landslide deposits are an intrinsically difficult and subjective operation that requires a great effort to minimise the inherent uncertainty. For the Staffora Basin, which extends for almost 300 km2 in the northern Apennines, three landslide inventory maps were independently produced by three groups of geomorphologists. In comparing each map with the others, large positional discrepancies arise (in the range of 55–65%). When all three maps are overlain, the locational mismatch of landslide deposit polygons increases to over 80%. To assess the impact of these errors on predictive models of landslide hazard, for the study area discriminant models were built up from the same set of geological-geomorphological factors as predictors, and the occurrence of landslide deposits within each terrain-unit, derived from each inventory map, as dependent variable. The comparison of these models demonstrates that statistical modelling greatly minimises the impact of input data errors which remain, however, a major limitation on the reliability of landslide hazard maps.


Author(s):  
M. Lebherz ◽  
W. Wiesbeck ◽  
W. Krank
Keyword(s):  

1980 ◽  
Author(s):  
Charles A. McNary ◽  
Diane K. Conti ◽  
Wilfried O. Eckhardt
Keyword(s):  

2018 ◽  
Vol 25 (2) ◽  
pp. 90-101 ◽  
Author(s):  
Julian S H Kwan ◽  
Harris W K Lam ◽  
Charles W W Ng ◽  
Nelson T K Lam ◽  
S L Chan ◽  
...  

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