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The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imager...
With a lot of successful applications of neural network-based classification, it has been recognized the classification can produce more accurate results than conventional approaches for remotely-sens...
Remote sensing techniques have been commonly used to map land cover and land use types. For many applications, users may only be interested in a specific land class in an image such as extracting urba...
With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a...
Four different data fusion methods for classifying SAR images with different spatial resolutions were tested and compared. Test area was Helsinki region in Southern Finland and test data 14 ERS-1/2 T...
This paper presents a study on automated habitat delineation and classification using very high spatial resolution (VHSR) optical remote sensing data in an alpine environment. Two overlapping study si...
We compared three multi-resolution merging techniques for classifying land cover and land condition in the central coast of California using Landsat7 imagery. We preferred the Principal Components t...
The growth of Dar es Salaam, Tanzania’s largest city, occurs predominantly through unplanned development. Given population growth rates that are estimated to be currently above 5% and the lack of res...
Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental change...

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