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Panchromatic (PAN) satellite imagery comprises only a single band but it has finer resolution in comparison to the multi-spectral band imagery. In the case of feature extraction and classification, although the multi-spectral imagery has an advantage in availability of the different aspect of spectral properties of the ground coverage, the recent studies introducing intelligent classification and...
There are two great challenges for classification of hyperspectral images (HSIs): lack in prior knowledge and serious internal-class variability. To address the issues, we propose a novel semisupervised method based on affinity scoring (AS). It can harness the fuzzy state of the contributions of spectral and spatial features to classification. The method consists of three major steps: over-segmentation,...
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,...
Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the...
In this paper we propose a new methodology to automatically generate retrospective high resolution land cover maps on a regular basis for the whole territory of Ukraine. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is used for multi-temporal Landsat-4/5/7 satellites imagery classification with previously restored missing data due to clouds, shadows and non-regular coverage...
This paper introduces a new statistical hypothesis test for image classification based on the geodesic distance. We present how it can be used for the classification of texture image. The proposed method is then employed for the classification of Polarimetric Synthetic Aperture Radar images of maritime pine forests on both simulated data with the PolSARproSim software and real data acquired during...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
Classification of signals acquired by condition monitoring systems for automotive application is becoming increasingly important. The work presented in this paper is motivated by a real-life classification challenge organized by Ford. Data samples from an automotive subsystem were collected. A classifier is designed to robustly isolate the different types of problems, by analyzing the acquired signals...
Fall is a leading cause of accidental injury deaths and a key cause of significant health problems, especially for elderly people who live alone. To assist those people for seeking help when falling and keeping records of key daily movements, we propose a simple yet effective system to monitor the daily activities and in-house locations using smartphone. We also test the system for the optimum arrangement...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
This paper presents a quadratic neural unit with error backpropagation learning algorithm to classify electrocardiogram arrhythmia disease. The electrocardiogram arrhythmia classification scheme consists of data acquisition, feature extraction, feature reduction, and a quadratic neural unit classifier to discriminate three different types of arrhythmia. A total of 44 records were obtained from MIT-BIH...
Feature selection is based on the notion that redundant and/or irrelevant variables bring no additional information about the data classes and can be considered noise for the predictor. As a result, the total feature set of a dataset could be minimized to only few features containing maximum discrimination information about the class. Classification accuracy is used as the evaluation measure in guiding...
As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to play important parts of our life, they have become more susceptible to attacks. In recent years, phishing and malicious websites have increasingly become serious problems in the field of network security. Attackers use many approaches to implant...
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate...
Dimension reduction of data is an important theme in the data processing. Reduct in the rough set is useful which has the same discernible power as the entire features in the higher dimensional scheme. But, classification with higher accuracy is not obtained in the reduct followed by nearest neighbor processing. To attack the problem, it is shown that nearest neighbor relation with minimal distance...
Augmenting spectral features with spatial features for hyperspectral image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial features from neighboring pixels. However, the resulting high dimensional input data, which are often difficult and expensive to obtain, require large quantities of labeled data to train a robust...
We present an ensemble method to classify Parkinson patients and healthy people. C&R Tree, Bayes Net and C5.0 are used to generate ensemble method. Using supervised learning technique, the proposed method generates rules to distinguish Parkinson patients from healthy people. The proposed method uses single classifier to generate rules which are used as input for the next used classifier and in...
In this paper I will present a technique to generate a digital signature for an image, which will uniquely identify it, using Radon transform. Even if Radon based approaches are broadly applicable to tomography (the construction of an image from the projection data related with cross-sectional scans of it), in this research work I will show how it can be successfully utilized to classify images and...
In the field of precision agriculture (PA), Un-manned Aerial Vehicles (UAVs) are creating new opportunities for remotely assessing various characteristics of crops. In this paper, we present two main contributions that were evaluated on a novel application: mapping red clover ground cover (RCGC). First, we develop an integrated system for collecting, pre-processing and analyzing aerial data for the...
Social media is widely used as a channel of communication in general purposes, including the comment that are related to retail business. It is a highly effective communication tool for direct interacting with their customers. Growth rate of the users is rapidly increasing, because they use this channel to receive information and share something interesting. In this paper, we present a comparison...
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