m. mokhtarzade

Accurate land cover classification from the digital surface model (DSM) obtained from LiDAR sensors is a challenging topic that researchers have considered in recent years. In general, the classification accuracy of land covers leads to low accuracy using a single-band DSM image. Hence, it seems necessary to develop efficient methods to extract relevant spatial information, which improves classification accuracy. In this regard, using spatial features based on morphological profiles (MPs) has significantly increased classification accuracy. Despite MPs' efficiency in increasing the DSM's classification accuracy, the classification accuracy results under the situation of limited training samples are not still at satisfactory levels. The main novelty of this paper is to propose a new feature space based on local kernel descriptors obtained from MP for addressing the mentioned challenge of MP-based DSM classification. These innovative feature vectors consider local nonlinear dependencies and higher-order statistics between the morphological features. The experiments of this study are conducted on two well-known DSM datasets of Houston and Trento. Our results show that support vector machine (SVM)-based DSM classification with the new local kernel features achieved an average accuracy of 93.75%, which is much better than conventional SVM classification with single-band DSM and MP features (by about 57% and 11.5% on average, respectively). Additionally, our proposed method outperformed two other DSM classification methods by an average of 4.7%.

Face recognition (FR) is a challenging computer vision task due to various adverse conditions. Local features play an important role in increasing the recognition rate of an FR method. In this direction, the covariance descriptors of Gabor wavelet features have been one of the most prominent methods for accurate FR. Most existing methods rely on covariance descriptors of Gabor magnitude features extracted from single-scale face images. This study proposes a new method named multiscale Gabor covariance-based ensemble Log-euclidean SVM (MGcov-ELSVM) for FR that uses the covariance descriptors of Gabor magnitude and phase features derived from multiscale face representations. MGcov-ELSVM begins by producing multiscale face representations. Gabor magnitude and phase features are derived from the multiscale face images in the second stage. After that, the Gabor magnitude and phase features are used to generate covariance descriptors. Finally, Covariance descriptors are classified via a log-Euclidean SVM classifier, and a majority voting technique determines the final recognition results. The experimental results from two face databases, ORL and Yale, indicate that the MGcov-ELSVM outperforms some recent FR methods.

Nowadays, hyperspectral images (HIs) are widely used for land cover land use (LCLU) mapping. Hyperspectral sensors collect spectral data in numerous adjacent spectral bands, which are usually redundant. Hyperspectral data processing comes with important challenges such as huge processing time, difficulties in transfer, and storage. In this study, two supervised and unsupervised dimensionality reduction methods are proposed for hyperspectral feature extraction based on the band clustering technique. In the first method, the unsupervised method, after the unsupervised band clustering stage with some statistical attributes, the principal component transform is used in each cluster, and the first PC component is considered an extracted feature. In the second method, the supervised method, bands are clustered based on training samples mean vectors of each class, and the weighted mean operator is used for feature extraction in each cluster. The experiment is conducted on the classification of real famous HI named Indian Pines. Comparing the obtained results and some other state of art methods proved the proposed method's efficiency.

One of the consequences of a fire is smoke. Occasionally, monitoring and detection of this smoke can be a solution to prevent occurrence or spreading a fire. On the other hand, due to the destructive effects of the smoke spreading on human health, measures can be taken to improve the level of health services by zoning and monitoring its expansion process. In this paper, an automated method is proposed to detect the dilute smoke caused by large fires in multispectral images. The main idea of this method is the impossibility of precisely reconstructing the smoke in the bands affected by smoke (blue band) using regression models from other spectral bands. In the first step of the proposed method, the absolute value of the residuals of the regression estimation of blue spectral band is transformed into a binary mask with the help of Otsu thresholding. Afterwards, in an iterative process, non-smoke areas are detected and then clustered. In the iteration process, a regression model is fitted for each cluster and for each pixel, coefficients with the least error of the blue band reconstruction is used. Through more accurate estimation of the blue band, it reduces the effect of First Positive Error and leads the mask of residuals obtained from thresholding process to the smoke areas. The final step of the proposed method is to refine and remove the incorrect image segments. This method has been successful in detecting diluted smokes and also in disregarding smoke in non-smoky images. The results show the average accuracy of  99.04 percent in several datasets with diluted smokes.