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Hyperspectral imagery generates huge data volumes, consisting of hundreds of contiguous and often highly redundant spectral bands. Difficulties are caused by this high dimensionality. Feature selection (FS) is a possible strategy to reduce the number of bands, consisting in selecting the most relevant bands for a classification problem. It is adapted to the design of superspectral sensor dedicated...
This paper introduces the concept of using a performance measure to select band groups when using a single classifier. Typically, band groups are selected using a proximity measure to determine the similarity or dissimilarity of hyperspectral bands. The problem with that approach is the similarity or dissimilarity of the hyperspectral bands may not be important for some problems. The novelty of using...
Positive and unlabeled learning (PUL) algorithm, an one-class classifier which is trained by positive samples and unlabeled samples, has been used in remote sensing classification. However, the effect of training strategy of PUL has not been investigated. This study tested the performances of PUL-SVM on cropland mapping by Landsat TM data using the training samples with different sizes and different...
The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We...
Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of hyperspectral images. In this paper, a rough set based supervised method is proposed to select informative bands in hyperspectral images. The proposed technique exploits...
Big Data Analytics methods take advantage of techniques from the fields of data mining, machine learning, or statistics with a focus on analysing large quantities of data (aka ‘big datasets’) with modern technologies. Big data sets appear in remote sensing in the sense of large volumes, but also in the sense of an ever increasing amount of spectral bands (i.e., high-dimensional data). The remote sensing...
In this paper, we propose a new method for hyperspectral image (HSI) classification using multi-layer superpixel graph and loopy belief propagation. A merging algorithm using graph based representation of image is applied to generate multi-scale superpixels in hyperspectral image at first. Then, we build a multi-layer superpixel graph and use loopy belief propagation to transmit messages between the...
The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for urban land cover in Wuhan, Hubei province of China. Firstly, radiation rectification, normalization processing and geometry registration are made between the bi-temporal images. Secondly, SVM approach is used in...
We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
The paper describes an experimental study on emotion recognition using a collection of emotional recordings from SRoL corpus. Its goal is to study and to obtain a simple tool that can be used in recordings validation in the process of building large voice corpora. The tools can help or even replace the human validation. In this study we used two classifiers, k-NN (k — Nearest Neighborhood) and SVM...
In the article a vision system for shape and colour recognition of dishes (plates, bowls, mugs), which can be used to automate the process of customer service in a self-service canteen is described. In consists of three basic components: object segmentation using so-called background model subtraction, shape recognition using geometric invariant moments and SVM classifier, as well as colour recognition...
Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter...
The paper presents a concise report on the comparison of the classifiers k-NN and SVM in the case of a fuzzy classification of the arterio-venous fistula based on audio recordings. What has been used in the studies are the acoustic signals taken from both healthy patients as well as those diagnosed with the narrowing of a fistula in a mild and major degree of stenosis. In the publication there have...
This paper presents regional Support Vector Machine (SVM) classifiers with a spatial model for object detection. The conventional SVM maps all the features of training examples into a feature space, treats these features individually, and ignores the spatial relationship of the features. The regional SVMs with a spatial model we propose in this paper take into account a 3-dimentional relationship...
The two classical steps of image or video classification are: image signature extraction and assignment of a class based on this image signature. The class assignment rule can be learned from a training set composed of sample images manually classified by experts. This is known as supervised statistical learning. The well-known Support Vector Machine (SVM) learning method was designed for two classes...
The demand of human identification in a non-intrusive manner has risen increasingly in recent years. Several works have already been done in this context using gait-cycle detection from human skeleton data using Microsoft Kinect as a data capture sensor. In this paper we have proposed a novel method for automatic human identification in real time using the fusion of both supervised and unsupervised...
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone),...
Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features,...
Monitoring of rock fragmentation is a commercially important problem for the mining industry. Existing analysis methods either resort to physically sieving rock samples, or using image analysis software. The currently available software systems for this problem typically work with 2D images and often require a significant amount of time by skilled human operators, particularly to accurately delineate...
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