The Experts below are selected from a list of 15672 Experts worldwide ranked by ideXlab platform
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo BruzzoneAbstract:Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with Active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an Active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art Active Learning methods.
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A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:In this paper, a novel iterative Active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.
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a genetic algorithm based cost sensitive Active Learning Technique for classification of remote sensing images
2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper proposes a novel cost-sensitive Active Learning (CSAL) Technique for the classification of remote sensing images with Support Vector Machines. The proposed Technique assumes that the labeling cost of samples during ground survey depends on both the samples accessibility and the traveling time to the considered locations. Thus, it is not equal for the samples on the ground. Accordingly, the proposed method aims at selecting the most informative (the most uncertain and diverse) as well as cost-efficient samples at each iteration of the Active Learning process. This is accomplished according to three steps. In the first step the most uncertain unlabeled samples are selected by using the multiclass-level uncertainty Technique. In the second step, the small (and important) portion of the image, in which the highest density of the most informative samples exists, is selected to effectively limit the study area. The objective of restricting the study area to a small portion of the image is to reduce the traveling time for labeling the samples. This is achieved on the basis of a novel clustering based approach. In the third step, uncertain samples that are diverse and cost-efficient are selected from the small portion of the image chosen at the second step. The selection of cost-efficient diverse samples is achieved on the basis of a genetic algorithm. Thanks to the second and third steps of the proposed CSAL Technique, the cost of sample labeling is significantly reduced, while obtaining accurate classification maps. Experimental results show the success of the proposed CSAL method compared to the most promising literature Active Learning methods.
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a novel som based Active Learning Technique for classification of remote sensing images with svm
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode Active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other Active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
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IGARSS - A novel SOM-based Active Learning Technique for classification of remote sensing images with SVM
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode Active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other Active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
Swarnajyoti Patra - One of the best experts on this subject based on the ideXlab platform.
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A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo BruzzoneAbstract:Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with Active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an Active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art Active Learning methods.
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A fast partition-based batch-mode Active Learning Technique using SVM classifier
Soft Computing, 2017Co-Authors: Anshu Singla, Swarnajyoti PatraAbstract:The selection of informative samples known as query selection is the most challenging task in Active Learning. In this article, a batch-mode Active Learning Technique is presented by defining a novel query function. The proposed Technique first divides the unlabeled samples into uniform partitions in one-dimensional feature space according to their distribution in the original feature space. Then to select the most informative samples from the unlabeled pool, one sample from each partition is selected based on an uncertainty criterion defined by exploiting SVM classifier. The number of unlabeled samples selected at each iteration of Active Learning is determined automatically and depends on the number of non-empty partitions generated. The effectiveness of the proposed Technique is measured by comparing it with four state-of-the-art Techniques exist in the literature by using four different UCI repository data sets. The experimental analysis proved that the proposed Technique is robust and computationally less demanding.
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A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:In this paper, a novel iterative Active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.
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a novel som based Active Learning Technique for classification of remote sensing images with svm
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode Active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other Active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
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IGARSS - A novel SOM-based Active Learning Technique for classification of remote sensing images with SVM
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode Active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other Active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
Isna Rahmawati - One of the best experts on this subject based on the ideXlab platform.
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TEACHING ENGLISH USING Active Learning Technique TO THE THIRD YEAR STUDENTS IN MI MUHAMMADIYAH PUCANG TULUNG KLATEN IN 2009/2010 ACADEMIC YEAR
2010Co-Authors: Isna RahmawatiAbstract:The objective of the study is to describe the process of teaching English using Active Learning Technique, to find the strength and weakness of this Technique. The result of the study is intended to be a little contribution to the teaching English. The writer formulates the problem in from of this research as follows: “how the process of teaching English using Active Learning Technique and what is is the strength and weakness of using Active Learning Technique”. The research was done in MI Muhammadiyah Pucang, Tulung, Klaten. As the sample, the writer took 14 students of the third year. Based on the data analysis the strengths of teaching English using Active Learning Technique are: (1) the students are very happy and fun; (2) they are not easy to be bored; (3) the students have the motivation to join the class and they are Active in the classroom; (4) the students have a high interest in teaching-Learning process; (5) it makes a good cooperation or relation among the students; (6) it makes the teacher creative. The weaknesses of teaching English using Active Learning Technique are: (1) the writer must give the instruction clearly; (2) the writer needs more work preparation of Active Learning than the others Technique; (3) it spends a lot of time because the writer must repeat and give the clear instructions to the students then she must guide them. The conclusion is teaching English using Active Learning Technique can develop the student’s knowledge in language skills including listening, speaking, reading, and writing. The students can be easy to understand, memorize, and pronounce the material given by the teacher.
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teaching english using Active Learning Technique to the third year students in mi muhammadiyah pucang tulung klaten in 2009 2010 academic year
2010Co-Authors: Isna RahmawatiAbstract:The objective of the study is to describe the process of teaching English using Active Learning Technique, to find the strength and weakness of this Technique. The result of the study is intended to be a little contribution to the teaching English. The writer formulates the problem in from of this research as follows: “how the process of teaching English using Active Learning Technique and what is is the strength and weakness of using Active Learning Technique”. The research was done in MI Muhammadiyah Pucang, Tulung, Klaten. As the sample, the writer took 14 students of the third year. Based on the data analysis the strengths of teaching English using Active Learning Technique are: (1) the students are very happy and fun; (2) they are not easy to be bored; (3) the students have the motivation to join the class and they are Active in the classroom; (4) the students have a high interest in teaching-Learning process; (5) it makes a good cooperation or relation among the students; (6) it makes the teacher creative. The weaknesses of teaching English using Active Learning Technique are: (1) the writer must give the instruction clearly; (2) the writer needs more work preparation of Active Learning than the others Technique; (3) it spends a lot of time because the writer must repeat and give the clear instructions to the students then she must guide them. The conclusion is teaching English using Active Learning Technique can develop the student’s knowledge in language skills including listening, speaking, reading, and writing. The students can be easy to understand, memorize, and pronounce the material given by the teacher.
Begum Demir - One of the best experts on this subject based on the ideXlab platform.
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A novel Active Learning Technique for multi-label remote sensing image scene classification
Image and Signal Processing for Remote Sensing XXIV, 2018Co-Authors: Bayable Teshome Zegeye, Begum DemirAbstract:This paper presents a novel multi-label Active Learning (MLAL) Technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL Technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL Technique redefines Active Learning by evaluating the informativeness of each image based on its multiple land-cover classes. Accordingly, the proposed MLAL Technique is based on the joint evaluation of two criteria for the selection of the most informative images: i) multi-label uncertainty and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the multi-label classification algorithm in correctly assigning multi-labels to each image, whereas multi-label diversity criterion aims at selecting a set of un-annotated images that are as more diverse as possible to reduce the redundancy among them. In order to evaluate the multi-label uncertainty of each image, we propose a novel multi-label margin sampling strategy that: 1) considers the functional distances of each image to all ML-SVM hyperplanes; and then 2) estimates the occurrence on how many times each image falls inside the margins of ML-SVMs. If the occurrence is small, the classifiers are confident to correctly classify the considered image, and vice versa. In order to evaluate the multi-label diversity of each image, we propose a novel clustering-based strategy that clusters all the images inside the margins of the ML-SVMs and avoids selecting the uncertain images from the same clusters. The joint use of the two criteria allows one to enrich the training set of images with multi-labels. Experimental results obtained on a benchmark archive with 2100 images with their multi-labels show the effectiveness of the proposed MLAL method compared to the standard AL methods that neglect the evaluation of the uncertainty and diversity on multi-labels.
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a genetic algorithm based cost sensitive Active Learning Technique for classification of remote sensing images
2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper proposes a novel cost-sensitive Active Learning (CSAL) Technique for the classification of remote sensing images with Support Vector Machines. The proposed Technique assumes that the labeling cost of samples during ground survey depends on both the samples accessibility and the traveling time to the considered locations. Thus, it is not equal for the samples on the ground. Accordingly, the proposed method aims at selecting the most informative (the most uncertain and diverse) as well as cost-efficient samples at each iteration of the Active Learning process. This is accomplished according to three steps. In the first step the most uncertain unlabeled samples are selected by using the multiclass-level uncertainty Technique. In the second step, the small (and important) portion of the image, in which the highest density of the most informative samples exists, is selected to effectively limit the study area. The objective of restricting the study area to a small portion of the image is to reduce the traveling time for labeling the samples. This is achieved on the basis of a novel clustering based approach. In the third step, uncertain samples that are diverse and cost-efficient are selected from the small portion of the image chosen at the second step. The selection of cost-efficient diverse samples is achieved on the basis of a genetic algorithm. Thanks to the second and third steps of the proposed CSAL Technique, the cost of sample labeling is significantly reduced, while obtaining accurate classification maps. Experimental results show the success of the proposed CSAL method compared to the most promising literature Active Learning methods.
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a cost sensitive Active Learning Technique for the definition of effective training sets for supervised classifiers
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper presents a novel cost-sensitive Active Learning Technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard Active Learning methods, the proposed Technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL Technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard Active Learning methods that neglect the labeling cost.
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IGARSS - A cost-sensitive Active Learning Technique for the definition of effective training sets for supervised classifiers
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper presents a novel cost-sensitive Active Learning Technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard Active Learning methods, the proposed Technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL Technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard Active Learning methods that neglect the labeling cost.
Luca Minello - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm based cost sensitive Active Learning Technique for classification of remote sensing images
2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper proposes a novel cost-sensitive Active Learning (CSAL) Technique for the classification of remote sensing images with Support Vector Machines. The proposed Technique assumes that the labeling cost of samples during ground survey depends on both the samples accessibility and the traveling time to the considered locations. Thus, it is not equal for the samples on the ground. Accordingly, the proposed method aims at selecting the most informative (the most uncertain and diverse) as well as cost-efficient samples at each iteration of the Active Learning process. This is accomplished according to three steps. In the first step the most uncertain unlabeled samples are selected by using the multiclass-level uncertainty Technique. In the second step, the small (and important) portion of the image, in which the highest density of the most informative samples exists, is selected to effectively limit the study area. The objective of restricting the study area to a small portion of the image is to reduce the traveling time for labeling the samples. This is achieved on the basis of a novel clustering based approach. In the third step, uncertain samples that are diverse and cost-efficient are selected from the small portion of the image chosen at the second step. The selection of cost-efficient diverse samples is achieved on the basis of a genetic algorithm. Thanks to the second and third steps of the proposed CSAL Technique, the cost of sample labeling is significantly reduced, while obtaining accurate classification maps. Experimental results show the success of the proposed CSAL method compared to the most promising literature Active Learning methods.
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a cost sensitive Active Learning Technique for the definition of effective training sets for supervised classifiers
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper presents a novel cost-sensitive Active Learning Technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard Active Learning methods, the proposed Technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL Technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard Active Learning methods that neglect the labeling cost.
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IGARSS - A cost-sensitive Active Learning Technique for the definition of effective training sets for supervised classifiers
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper presents a novel cost-sensitive Active Learning Technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard Active Learning methods, the proposed Technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL Technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard Active Learning methods that neglect the labeling cost.
