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The high-dimensional feature vectors of hyper spectral data often impose a high computational cost as well as the risk of "over fitting" when classification is performed. Therefore it is necessary to ...
A new dynamical dimensional reduction model (HDRM) for hyperspectral images is proposed based on clone selection algorithm which is inspired from nature immune system in this paper. In existing dimens...
Assessing the quality of a hyperspectral image is a difficult task. However, this assessment is required at different levels of the instrument design: evaluation of the signal to noise ratio necessary...
The ability of hyperspectral image (HSI) data to provide estimates of soil electrical conductivity (ECa) and soil fertility levels without requiring extensive field data collection was investigated....
In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original i...
This paper combine two conventional feature extraction methods (NWFE&NPE) in a novel framework and present a new semi-supervised feature extraction method called Adjusted Semi supervised Discriminant ...
In the most applications in remote sensing, there is no need to use all of available data, such as using all of bands in hyperspectral images. In this paper, a new band selection method was proposed t...
Pansharpening has proven to be a valuable method for resolution enhancement of multi-band images when spatially high-resolving panchromatic images are available in addition. In principle, pansharpenin...

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