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A method for electromagnetic radiation source identification is proposed. The spatial characteristic of a radiation source is taken as the unique parameter for support vector machines (SVMs) to identify. First, the location of radiation source is determined by the triangulation method, and then its spatial characteristic is collected by a band receiver array with simulation, which removes the limit...
Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research....
This paper describes an approach to automatically detect the gender of Twitter users, based only on clues provided by their profile information in an unstructured form. A number of features that capture phenomena specific of Twitter users is proposed and evaluated on a dataset of about 242K English language users. Different supervised and unsupervised approaches are used to assess the performance...
This paper proposes a reliable and efficient method for recognition in two different orientations (either left or right) by Magnetoencephalograph (MEG) signals. The brain activities are measured using different approaches with different spatial and temporal resolutions. The MEG signals are usually used for brain-computer interface (BCI) applications due to high temporal resolution. The MEG signals...
The main goal of this paper is to explore the recognition of particular guitar models from single instrument audio recordings. This is different than existing work in music instrument recognition that deals with identifying different instrument types. Through a set of experiments we evaluate different sets of audio features and classifiers for this purpose. To improve accuracy a composite classifier...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Land-use change is one of main factors of coastal erosion. This research aimed to integrate techniques of remote sensing and geographic information system to investigate the relationship between land-use changes and coastal erosion in Phuket Island, Thailand, using multi-sensors and temporal imageries acquired during 2003–2011. Eight land-use classes including built-up, forest, mangrove forest, agriculture,...
There is a need for rapid response during disasters. However, there is a paucity of training data which leads to classification models that do not generalize well. If the pre disaster data is used to augment the training data, the models perform poorly due to statistical distribution differences between pre and post disaster conditions. Also, it is challenging to analyze large areas for identifying...
This paper proposes a novel method for classification of Remote Sensing images. In this method, the popular Maximum Likelihood Classifier (MLC) combined with the Support Vector Machine (SVM) classifier. This method computes the energy function of Markov Random Field (MRF) in the neighborhoods of the test pixels. Then, relates the Markovian energy-difference function to the SVM classifier. Therefore,...
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Most classification methods focus on various combination of features and ignore the similarities between different categories. In this paper we present a modification by combining ScSPM [1] with a dictionary learning method DL-COPAR [2], which separates the particularity and commonality...
A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI),...
With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification,...
In this paper, a new All-Convolutional Networks (A-ConvNets) is proposed and applied to Moving and Stationary Target Acquisition and Recognition (MSTAR) data. Conventional deep learning algorithms, especially the deep convolutional networks (ConvNets) have achieved many success state-of-art results. However, directly applying ConvNets to SAR data will yield severe overfitting because of limited data...
Recently, the superpixel segmentation is introduced into the hyperspectral image (HSI) classification to exploit the spatial information. However, the size of superpixels influences the classification significantly because small superpixels can not provide enough spatial information and large superpixels generally result in error segmentation. The error segmentation is irreversible and intolerable,...
Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. The state-of-the-art methods, e.g. bag-of-visual-words model and its various extensions as well as the topic models, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and the key procedure which has a great influence on the results...
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the...
This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results...
Feasibility of Random Forest and Support Vector Machine classifiers is tested for the discrimination of 7 types of vegetation near lake Poosjärvi in Western Finland. Four sets of features grouped as basic, textural, ICA or PCA based, and rotational features are applied. The results indicate that the Random Forest classification scheme outperforms the Support Vector Machine classifier. For both classifiers...
With this work, we present a method for the detection of alpine permafrost surface deformations by using DInSAR (Differential SAR Interferometry) technique, integrating RADARSAT-2 and COSMO-SkyMed data through Support Vector Machine (SVM). On our test dataset, the combination of the two sensors produces an increase of classification accuracy equal to 8.5% with respect to the case in which only one...
Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers...
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