Farhad Samadzadegan

TL;DR: The experimental results and related indices show that the proposed algorithm can find optimum tour according to introduced constraints.

TL;DR: Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images and a wide range of algorithms are investigated, including 41 methods investigated, which indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based method had the highest spatial quality and CS- based methods had the lowest results both spectrally and spatially.

TL;DR: The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

TL;DR: Proposed method in this paper is based on combining multiple classifiers (MCS) is one of the most important topics in pattern recognition to achieve higher accuracy, since many tasks related to automatic scene interpretation are involved.

TL;DR: The results clearly show the superiority of the hyperspectral signal subspace identification by minimum, second moment linear, and noise-whitened Harsanyi-Farrand-Chang estimators, also the principal component analysis and independent component analysis as DR techniques, and the norm L1 and Euclidean distance metrics to process hyperspectrals imagery by using the K-NN classifier.