The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. When classifying hyperspectral and multispectral images with the existing algorithms, we use only the spectral information more often. This paper presents an one-class...
High dimensionality of hyperspectral data and relatively limited training samples induce the Hughes phenomenon in hyperspectral image classification. To prevent this problem and decrease the computational cost, feature extraction often acts as pre-processing. In this paper, a subspace weighting kernel method combining clustering-based grouping is proposed for feature extraction in hyperspectral imagery...
Anomaly detection is one of the most important applications for hyperspectral images. Conventional algorithm such as Reed-Xiaoli (RX) detector fails to be applied to hyperspectral images, which have high spectral dimensionality and complicated correlation between spectral bands. Therefore, effective feature extraction methods and selection rules are necessary. In this paper, comparative analyses of...
Small target detection in infrared imagery with complex background is always an important task in infrared target tracking system. Complex clutter background usually results in serious false alarm because of low contrast of infrared imagery. In this paper, a composite kernel regression method is proposed for infrared small target detection. In the proposed method, a nonlinear regression model is firstly...
The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire...
Target detection with SAR image is one of important research topics in remote sensing. In this paper, a kernel regression-based predicting method is proposed for target detection in SAR image. Badly speckle noise and background clutter are two main factors which make the target detection with SAR image difficult. In the proposed method, the kernel regression on local image is used to exactly predict...
Short-term load forecasting is very important for power system. A combined excellent model based on least squares support vector machines in Bayesian inference is proposed in this paper to do the short-term load forecasting. Least squares support vector machines (LS-SVM) are new kinds of support vector machines (SVM) which regress faster than the standard SVM, they are adopt to do the forecasting,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.