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.
Active learning (AL) has obtained a great success in supervised remotely sensed hyperspectral image classification, since it is able to select highly informative training samples. As an intrinsically biased sampling approach, AL generally favors the selection of samples following discriminative distributions, which are located in low-density areas. However, hyperspectral data are often highly class-mixed,...
Convolutional neural networks (CNNs) have shown great potential for remote sensing image classification. As the features obtained from a deep CNN generally exhibit high generalization capacity, the subsequent classifier is normally able to provide good results without the need for careful optimization. However it is well-known that, in the pursuit of high classification results, it is generally difficult...
Though in the era of big data, it remains a challenge to be tackled that the forecasting model with high accuracy and robustness needs to be built using small size samples. One effective tool of addressing this problem is the virtual sample generation (VSG), which can generate a mass of new virtual samples on the basis of small sample sets. The bootstrap method is adopted to feasibly resample the...
The methodology of sparse representations (SRs) has being popular in hyperspectral image (HSI) classification. To boost the SR-based classification for HSIs, in this paper we present a designation of sparse representation involving random subspace. First, random band selection or random projection generates data subspaces from an original HSI. Then, the sparse representation on each subspace is solved...
Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data...
In flexible naïve Bayesian (FNB), the excellent qualities of Gaussian kernel have been demonstrated by the theoretical analyses and experimental comparisons with normal naïve Bayesian (NNB). There are also several types of kernel functions commonly used for probability density estimation, i.e., uniform, triangular, epanechnikov, biweight, triweight and cosine. We call them discontinuous kernels. In...
It has been verified that hyperspectral data is statistically characterized by elliptical symmetric distribution. Accordingly, we introduce the ellipsoidal discriminant boundaries and present an elliptical symmetric distribution based maximal margin (ESD-MM) classifier for hypespectral classification. In this method, the characteristic of elliptical symmetric distribution (ESD) of hyperspectral data...
This paper explores from the security problems about whether fix patches and selective repair of the vulnerabilities, which takes computer vulnerabilities and patches associated into comprehensive consideration. Through selecting some key factors as attributes vectors, I propose the selective vulnerability - patch associated repair model by using support vector machine (SVM) classification method...
Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification...
P300 is one of the most studied components of event related potentials which reflects the responses of brain to events in the external environment. In this paper, we present a new method that utilizes multiresolution autoregression of multichannel time series (MAMTS) for feature extraction of P300 wave. First, it adopts multiresolution autoregression on dyadic tree to depict the characteristic of...
In this paper, we theoretically analyze the limitation of the Quotient Image (QI) method proposed by Shashua and present a new way to compute the quotient image. Based on the observation that nine basis point light sources can represent almost arbitrary lighting conditions for face recognition application, we use the corresponding nine basis images which are all real images to span the image space...
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.