Hyperspectral sensors are important tools in monitoring the phenomena of the Earth due to the acquisition of a large number of spectral bands. Hyperspectral image classification is one of the most important fields of hyperspectral data processing, and so far there have been many attempts to increase its accuracy. Spatial features are important due to their ability to increase classification acc...

In this paper a new efficient method for detecting the impulse noise from the corrupted image using extreme learning machine (ELM) is proposed. An improved version of the standard median filter is suggested to remove the detected noisy pixel. The performance of proposed detector is evaluated using classification accuracy. The results show that our detector is robust even at higher noise density...

      In this research, the capability of Rapid Eye satellite imagery for mapping the crown distribution of oak trees in Zagros forests was investigated in the Dashtebarm forest area of ​​Kazeroun, Fars province. In this study, data quality was investigated geometrically and radiometrically and geometric correction of the images was done using a linear method and using precision ground control ...

Remote sensing has found a special technique and effective in the geological studies and mineral identification for the determination of minerals in the primary detection. The purpose of this study is to recognize the high purity calcite mineral regions in carbonate units using Aster and sentinel 2 images. Sampling of rocks and laboratory analysis using XRF and XRD for verification. The results...

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, corr...