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Classification of Land Use Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
Extracting man-made objects in satellite images which are generated from the meter to sub-meter resolution plays an important role in remote satellite image analysis. However, spectral characteristics of urban land objects are so similar. So the classification accuracies are far from satisfactory by using only spectral information. As a result, researchers turn to incorporate geometrical information...
In recent years high-resolution space borne images have disclosed a large number of new opportunities for medium and large-scale rubber plant mapping. Some traditional algorithms used for hyper spectral remote sensing image classification have some problems such as low computing rate, low accuracy. According to SVM theory, the Rubber plant classification model based on SVM was constructed, by experimenting...
This work is an evaluation, to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation...
A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present...
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