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.
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM),...
Remote sensing is the method used to detect and measure target characteristics using electromagnetic energy in the form of heat, light and radio waves. Different applications where remote sensing is used are agriculture, disaster management, urban planning, water resource management, etc. The process of producing thematic map from remotely sensed imagery is called image classification. In one or more...
This paper presents a novel multikernel based Sparse representation for the classification of Remotely sensed images. The sparse representation based feature extraction are in a run which is a signal dependent feature extraction and thus more accurate. Multikernel Sparse representation was also had proved to be more accurate and less computationally complex while implemented in other applications...
In hyperspectral image classification, finding the best criterion for separating classes and assign a accurate label to pixels is a major challenge. In traditional classification methods, as SVM, SAM, KNN the uniform criterion is considered for separating the classes as margin, angle, distance, etc. In classification process the distance between unlabeled pixel and a class is calculated. The minimum...
Many studies [1]–[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]–[5] show that kernel method is computationally efficient,...
Camera motion classification is an important research topic in video content analysis and retrieval. In this paper, we propose a nonparametric classification method of camera motion, which employs support vector machine to learn motion vector file and then train classifiers to categorize camera motion. Libsvm is used as support vector machine tool to validate the method and experiments demonstrate...
The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they...
In recent years high-resolution space borne images have disclosed a large number of new opportunities for medium and large-scale rubber plant mapping. Some traditional algorithms used for hyper spectral remote sensing image classification have some problems such as low computing rate, low accuracy. According to SVM theory, the Rubber plant classification model based on SVM was constructed, by experimenting...
A novel approach to active sampling is proposed for the semi automatic selection of training patterns in a given pool of candidates. In the method proposed, each candidate is ranked with a double criterion: first, informativeness of the candidate is assessed using the Support Vector Machine (SVM) real valued decision function. Then, diverse sampling is ensured by considering the relative position...
The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components. The inverse of the covariance matrix is computed in a closed form and is used in the kernel to compute the distance...
We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which...
Classification is one of the most important procedures in high-resolution remotely sensed image information extraction. This paper introduced Adaboost-SVM algorithm to IKONOS image classification. The classification performance of Adabost-SVM and single SVM were quantitatively analyzed and qualitatively evaluated. The results show that: In the case of small training samples, Adaboost-SVM outperforms...
Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number...
In this paper we propose using histogram intersection for mammographic image classification. First, we use the bag-of-words model for image representation, which captures the texture information by collecting local patch statistics. Then, we propose using normalized histogram intersection (HI) as a similarity measure with the K-nearest neighbor (KNN) classifier. Furthermore, by taking advantage of...
Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative...
This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the...
We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature...
This paper presents the utilization of empirical mode decomposition (EMD) of hyperspectral images to increase the classification accuracy using support vector machine (SVM)-based classification. EMD has been shown in the literature to be particularly suitable for nonlinear and nonstationary signals and is used in this paper to decompose hyperspectral image bands into several intrinsic mode functions...
Support vector machine (SVM) is a machine learning algorithm, which has been used recently for classification of hyperspectral images. SVM uses various kernel functions like RBF and polynomial to map the data into higher dimensional space to improve data separability. New kernel functions are used in this paper to classify hyperspectral images which are based on wavelet functions as named wavelet-kernels...
This paper proposes to combine standard SVM classification with a hierarchical approach to increase SVM classification accuracy as well as reduce computational load of SVM testing. Support vectors are obtained by applying SVM training to the entire original training data. For classification, multi-level two-dimensional wavelet decomposition is applied to each hyperspectral image band and low spatial...
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.