in this paper, an unsupervised classification method using spatial contextual information for polarimetric sar (polsar) image classification is proposed. first, an unsupervised classification based on 2d h/▁α plane was performed, using cloude/pottier target decomposition algorithm. in order to compute the initial values of the cluster centers and hence a rapid convergence of the algorithm, the ...

This paper presents the use of satellite data (i.e., Landsat-5 & Landsat-8) to interpret change land cover from 1997 2020. The study area covers administrative boundary Lumajang Regency. land-cover map year derived Landsat-5. Land-cover 2020 interpreted Landsat-8. uses two methods image classifications unsupervised and supervised). procedure includes enhancement, registration, classificatio...

In this paper, we investigate whether microblogging texts (tweets) produced on mobile devices are related to the geographical locations where they were posted. For this purpose, we correlate tweet topics to areas. In doing so, classified points of interest from OpenStreetMap serve as validation points. We adopted the classification and geolocation of these points to correlate with tweet content...

In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs). We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. The K-mean clustering is u...

This paper presents a novel semisupervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learning. First, we propose the semi-supervised learning method of ADN. ADN is constructed by Restricted Boltzmann Machines (RBM) with unsupervised learning using labeled data and abundant of unlabeled data. Then the constru...

In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering highand very-high resolution data; yet on the other hand, the process of obtaining the ground truth da...

Feature selection has been extensively used in supervised learning, such as text classification. It (Devaney and Ram 1997) minimizes the high dimensionality of the feature space and also offers improved data understanding which enhances the clustering result. The chosen feature set should consist of adequate data about the original data set. It is believed that feature selection can enhance the...

Abstract— Widely used unsupervised classification method H/A/alpha classification, explores the scattering information of land-coverage data, but performs poorly on the decision boundary. Maximum Likelihood Supervised Classification using Wishart distribution, based on the statistic properties requiring picking up training set manually from large SAR image, can’t be automated. In this project, ...

This paper gives a brief description of a hierarchical architecture (HONG) that has been described elsewhere. The learning algorithm it uses is a mixed unsupervised/supervised method with most of the learning being unsupervised. The architecture generates multiple classifications for every data pattern presented, and combines them to obtain the final classification. The main purpose of this pap...

    Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a...