The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jon Atli Benediktsson - One of the best experts on this subject based on the ideXlab platform.
-
Advances in hyperspectral image classification: Earth monitoring with statistical Learning methods
IEEE Signal Processing Magazine, 2014Co-Authors: Gustavo Camps-valls, Lorenzo Bruzzone, Devis Tuia, Jon Atli BenediktssonAbstract:Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for Learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical Learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active Learning, to take advantage of the manifold structure with Semisupervised Learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.
-
Semisupervised self-Learning for hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Inmaculada Dopido, Antonio J. Plaza, Prashanth Reddy Marpu, José M. Bioucas-dias, Jun Li, Jon Atli BenediktssonAbstract:Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting Semisupervised Learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for Semisupervised Learning which adapts available active Learning methods (in which a trained expert actively selects unlabeled samples) to a self-Learning framework in which the machine Learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine–machine framework when compared with traditional machine–human active Learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible–Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-Learning represents an effective and pro- ising strategy in the context of hyperspectral image classification.
Feiping Nie - One of the best experts on this subject based on the ideXlab platform.
-
fast Semisupervised Learning with bipartite graph for large scale data
IEEE Transactions on Neural Networks, 2020Co-Authors: Feiping Nie, Rong Wang, Weimin JiaAbstract:As the captured information in our real word is very scare and labeling sample is time cost and expensive, Semisupervised Learning (SSL) has an important application in computer vision and machine Learning. Among SSL approaches, a graph-based SSL (GSSL) model has recently attracted much attention for high accuracy. However, for most traditional GSSL methods, the large-scale data bring higher computational complexity, which acquires a better computing platform. In order to dispose of these issues, we propose a novel approach, bipartite GSSL normalized (BGSSL-normalized) method, in this paper. This method consists of three parts. First, the bipartite graph between the original data and the anchor points is constructed, which is parameter-insensitive, scale-invariant, naturally sparse, and simple operation. Then, the label of the original data and anchors can be inferred through the graph. Besides, we extend our algorithm to handle out-of-sample for large-scale data by the inferred label of anchors, which not only retains good classification result but also saves a large amount of time. The computational complexity of BGSSL-normalized can be reduced to $O(ndm+nm^{2})$ , which is a significant improvement compared with traditional GSSL methods that need $O(n^{2}d+n^{3})$ , where $n$ , $d$ , and $m$ are the number of samples, features, and anchors, respectively. The experimental results on several publicly available data sets demonstrate that our approaches can achieve better classification accuracy with less time costs.
-
Semisupervised Learning with parameter free similarity of label and side information
IEEE Transactions on Neural Networks, 2019Co-Authors: Rui Zhang, Feiping NieAbstract:As for Semisupervised Learning, both label information and side information serve as pivotal indicators for the classification. Nonetheless, most of related research works utilize either label information or side information instead of exploiting both of them simultaneously. To address the referred defect, we propose a graph-based Semisupervised Learning (GSL) problem according to both given label information and side information. To solve the GSL problem efficiently, two novel self-weighted strategies are proposed based on solving associated equivalent counterparts of a GSL problem, which can be widely applied to a spectrum of biobjective optimizations. Different from a conventional technique to amalgamate must-link and cannot-link into a single similarity for convenient optimization, we derive a new parameter-free similarity, upon which intrinsic graph and penalty graph can be separately developed. Consequently, a novel Semisupervised classification algorithm can be summarized correspondingly with a theoretical analysis.
-
parameter free auto weighted multiple graph Learning a framework for multiview clustering and semi supervised classification
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Feiping NieAbstract:Graph-based approaches have been successful in unsupervised and semi-supervised Learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. In this paper, we propose a novel framework via the reformulation of the standard spectral Learning model, which can be used for multiview clustering and Semisupervised tasks. Unlike other methods in the literature, the proposed methods can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. Furthermore, our objective under Semisupervised Learning is convex and the global optimal result will be obtained. Extensive empirical results on different real-world data sets demonstrate that the proposed methods achieve comparable performance with the state-of-the-art approaches and can be used more practically.
-
Semisupervised Learning using negative labels
IEEE Transactions on Neural Networks, 2011Co-Authors: Chenping Hou, Feiping Nie, Fei Wang, Changshui ZhangAbstract:The problem of Semisupervised Learning has aroused considerable research interests in the past few years. Most of these methods aim to learn from a partially labeled dataset, i.e., they assume that the exact labels of some data are already known. In this paper, we propose to use a novel type of supervision information to guide the process of Semisupervised Learning, which indicates whether a point does not belong to a specific category. We call this kind of information negative label (NL) and propose a novel approach called NL propagation (NLP) to efficiently make use of this type of information to assist the process of Semisupervised Learning. Specifically, NLP assumes that nearby points should have similar class indicators. The data labels are propagated under the guidance of NL information and the geometric structure revealed by both labeled and unlabeled points, by employing some specified initialization and parameter matrices. The convergence analysis, out-of-sample extension, parameter determination, computational complexity, and relations to other approaches are presented. We also interpret the proposed approach within the framework of regularization. Promising experimental results on image, digit, spoken letter, and text classification tasks are provided to show the effectiveness of our method.
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
-
active and Semisupervised Learning for the classification of remote sensing images
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Claudio Persello, Lorenzo BruzzoneAbstract:This paper aims at analyzing and comparing active Learning (AL) and Semisupervised Learning (SSL) methods for the classification of remote sensing (RS) images. We present a literature review of the two Learning paradigms and compare them theoretically and experimentally when addressing classification problems characterized by few training samples (w.r.t. the number of features) and affected by sample selection bias. Commonalities and differences are highlighted in the context of a conceptual framework used to describe the workflow of the two approaches. We point out advantages and disadvantages of the two approaches, delineating the boundary conditions on the applicability of the two paradigms with respect to both the amount and the quality of available training samples. Moreover, we investigate the integration of concepts that are in common between the two Learning paradigms for improving state-of-the-art techniques and combining AL and SSL in order to jointly leverage the advantages of both approaches. In this framework, we propose a novel SSL algorithm that improves the progressive Semisupervised support vector machine by integrating concepts that are usually considered in AL methods. We performed several experiments considering both synthetic and real multispectral and hyperspectral RS data, defining different classification problems starting from different initial training sets. The experiments are carried out considering classification methods based on support vector machines.
-
Advances in hyperspectral image classification: Earth monitoring with statistical Learning methods
IEEE Signal Processing Magazine, 2014Co-Authors: Gustavo Camps-valls, Lorenzo Bruzzone, Devis Tuia, Jon Atli BenediktssonAbstract:Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for Learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical Learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active Learning, to take advantage of the manifold structure with Semisupervised Learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.
-
an unsupervised change detection technique based on bayesian initialization and Semisupervised svm
International Geoscience and Remote Sensing Symposium, 2007Co-Authors: Francesca Bovolo, Lorenzo Bruzzone, Mattia MarconciniAbstract:This paper presents a novel approach to unsupervised change detection, which is based on the combined use of the change vector analysis (CVA) technique and the Semisupervised support vector machine (S3VM) classification method. The proposed approach aims at analyzing the information present in multitemporal images by jointly analyzing their original spectral signatures. This is accomplished by using the CVA technique in a selective way for defining a pseudotraining set necessary for initializing the S3VM binary classifier. Then, starting from these initial seeds, the S3VM performs change detection in the original multitemporal feature space. This is done by gradually involving unlabeled multitemporal pixels in the Semisupervised Learning procedure for better modeling the decision boundary between changed and unchanged pixels. Experimental results obtained on different multispectral and multitemporal images confirm the effectiveness of the proposed approach.
Xiuping Jia - One of the best experts on this subject based on the ideXlab platform.
-
a novel Semisupervised active Learning algorithm for hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Zengmao Wang, Lefei Zhang, Liangpei Zhang, Xiuping JiaAbstract:Less training samples are a challenging problem in hyperspectral image classification. Active Learning and Semisupervised Learning are two promising techniques to address the problem. Active Learning solves the problem by improving the quality of the training samples, while Semisupervised Learning solves the problem by increasing the quantity of the training samples. However, they pay too much attention to the discriminative information in the unlabeled data, leading to information bias to train supervised models, and much more effort to label samples. Therefore, a method to discover representativeness and discriminativeness by Semisupervised active Learning is proposed. It takes advantages of both active Learning and Semisupervised Learning. The representativeness and discriminativeness are discovered with a labeling process based on a supervised clustering technique and classification results. Specifically, the supervised clustering results can discover important structural information in the unlabeled data, and the classification results are also highly confidential in the active-Learning process. With these clustering results and classification results, we can assign pseudolabels to the unlabeled data. Meanwhile, the unlabeled samples that cannot be assigned with pseudolabels with high confidence at each iteration are regarded as candidates in active Learning. The methodology is validated on four hyperspectral data sets. Significant improvements in classification accuracy are achieved by the proposed method with respect to the state-of-the-art methods.
Chenying Liu - One of the best experts on this subject based on the ideXlab platform.
-
superpixel based Semisupervised active Learning for hyperspectral image classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019Co-Authors: Chenying LiuAbstract:In this work, we propose a new Semisupervised active Learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via Semisupervised Learning. The Learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based Semisupervised Learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for Semisupervised Learning in hyperspectral image classification.
