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This paper proposes a change detection approach, called FVA (feature vector analysis), for multi- resolution images, which is based on calculating the shape simi- larity
During the application of object-level change detection method, first, feature vectors of objects are constructed by controlling the weight of radiation, texture and
The explicit modelling of such geometric correspondences allows not only for the connection of objects or object parts given in different types, geometric data
Figure 2 shows the sharp image on the left and a blurred image on the right, with blue lines show the connection between sharp and blurred feature points.. Figure 2 demonstrates
To estimate the volume of timber stack its frontal area is measured and some control parts of a stack are used for stacking coefficient (wood density in a stack)
The results of this process was the creation of two topographic feature surveying specifications documents – (a) a topographic feature list (see Figure 2) --
3.5 Stream Network/stream order/stream to feature: The stream network of the Upper Benue Trough generated from the stream direction grid and stream accumulation grid
A novel object based semantic point cloud labelling method util- ising the geometrical information from LiDAR point cloud data and spectral information from optical images has
