1,720,986 research outputs found
Gaussian process regression within an active learning scheme
No full textIn this work, we face the problem of training sample collection for the estimation of biophysical parameters by adopting the active learning approach. In particular, we propose two active learning strategies specifically developed for Gaussian Process (GP) regression. The first one is based on adding samples that are distant from the current training samples in the kernel space while the second one exploits an intrinsic GP regression outcome to pick up the most difficult samples. Experiments on simulated and real data sets show the effectiveness of active selection of training samples for regression problems. © 2011 IEEE
An approach for classifying large scale images
No full textIn the remote sensing field, classification of images at large scale represents a very important problem. Most of the proposed classification strategies are based on supervised methods, which can give excellent performances, but depend strongly on the training samples used to construct the classification model. In particular, they can fail if such samples are not representative of the distributions associated with the classes. This problem is critical in a large scale scenario, in which the training samples acquired from a limited region of the image, called source domain, are not representative for classifying samples extracted from a different region, called target domain. In this work, we propose to alleviate this problem by adopting an active learning approach, in which few additional samples are selected and labeled from the new domain in order to improve generalization capabilities of the model. In particular, we suggest implementing an initialization strategy before applying the traditional active learning process. The proposed approach is validated experimentally on a MODIS data set for the discrimination between vegetation and non-vegetation areas at European scale. © 2012 IEEE
Model-based active learning for svm classification of remote sensing images
No full textIn this work, we present a new support vector machine (SVM)-based active learning method for the classification of remote sensing images. Starting from an initial suboptimal training set, an iterative process defines the regions of significance in the feature space, then selects additional samples from a large set of unlabeled data and adds them to the training set after their manual labeling. Experimental results on a very high resolution (VHR) image show that the proposed method exhibits promising capabilities to select samples that are really significant for the classification problem, both in terms of accuracy and stability. © 2010 IEEE
Ground-truth assisted design for remote sensing image classification
No full textIn this work, we propose a framework to help in the design of the ground-truth for the classification of remote sensing images. It consists first to segment the considered image by means of a level set method and then to extract the segments characterized by the largest numbers of pixels. Afterward, the selected segments are labeled by a human user. Experimental results obtained on a very high resolution image show encouraging performances of the proposed framework. © 2011 IEEE
Swarm intelligence for unsupervised classification of hyperspectral images
No full textA new methodology for the unsupervised classification of hyperspectral images is proposed. Based on swarm intelligence, it addresses simultaneously two different issues which are: 1) the estimation of the cluster parameters; and 2) the detection of the best discriminative bands. For such purpose, it optimizes jointly two different criteria, which are the log likelihood function and the Bhattacharyya statistical distance between classes. Experimental results show that, despite the completely unsupervised nature of the proposed methodology, very encouraging performances in terms of classification accuracy can be achieved. ©2009 IEEE
Fusion of Multitemporal Contextual Information by Neural Networks for Multisensor Image Classification
No full textA pattern recognition system for extracting buried object characteristics in GPR images
No full textIn this work, we present a pattern recognition system for the automatic analysis of ground penetrating radar (GPR) images. This system comprises pre-processing, segmentation, object detection, object material recognition, and object dimension estimation stages. Object detection is done using an unsupervised strategy based on genetic algorithms (GA) which allows to localize linear/hyperbolic patterns in GPR images. Object material recognition is approached as a classification issue, which is solved by means of a support vector machine (SVM) classifier. Dimension estimation is formulated within a Gaussian process (GP) regression approach. Results on synthetic images, representing random exploration scenarios, are reported and discussed. ©2009 IEEE
SVR active learning for product quality control
No full textIn this work, the active learning approach is adopted to address the problem of training sample collection for the estimation of chemical parameters for product quality control from spectroscopic data. In particular, two strategies for support vector regression (SVR) are proposed. The first method select samples distant in the kernel space from the current support vectors, while the second one uses a pool of regressors in order to choose the samples with the greater disagreements between the different regressors. The experimental results on two real data sets show the effectiveness of the proposed solutions. © 2012 IEEE
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