With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.

The presence of noise, loss of information or feature nonstationarity in data is the limiting factor for many machine learning decision systems. Previous research has shown that relevant feature selection may be helpful to alleviate the impact of these possible perturbations. This paper presents a dynamical feature subspaces selection method based on ensembles of one-class Support Vector Machine (SVM), with the objective to optimize the performance of a decision system in such a nonstationary environment. Our method is predicated on the assumption that only the performance of the classifiers using perturbed features is degraded. We propose a mechanism for constructing an ensemble of classifiers based on a large number of feature subspaces generated from the initial full-dimensional space. In the phase of classification, the ensemble system is capable to select adaptively feature subspaces which are supposed to be immune to the nonstationary disturbance and to make the final decision by combining the individual decisions of classifiers built in these subspaces. One characteristic of this method is that we use the one-class SVM ensemble to accomplish simultaneously the tasks of feature subspace selection and classification. The effectiveness of the proposed method has been demonstrated through the experiments conducted in the context of textured image classification.

In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.

Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial-contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper.

In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral remote sensing image classification for the first time. The framework of Rotation Forest is to project the original data into a new feature space using transformation methods for each base classifier (decision tree), then the base classifier can train in different new spaces for the purpose of encouraging both individual accuracy and diversity within the ensemble simultaneously. Principal component analysis (PCA), maximum noise fraction, independent component analysis, and local Fisher discriminant analysis are introduced as feature transformation algorithms in the original Rotation Forest. The performance of Rotation Forest was evaluated based on several criteria: different data sets, sensitivity to the number of training samples, ensemble size and the number of features in a subset. Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest. They indicate that Rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed Split Augmented Lagrangian Shrinkage (SALSA) algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared to the state-of-the-art.

Support vector machine (SVM) and Random Forest (RF) have been developed to improve the accuracy of hyperspectral remote sensing (HRS) image classification significantly in recent years. Due to the different characteristics and obvious diversity between SVM and RF, we propose two integration approaches which combine SVM and Random Forest to classify the HRS image. The proposed method called DWDCS is examined by two hyperspectral images and it can acquire the higher overall accuracy and also improve the accuracy of each classes. Experimental results indicate that the proposed approaches have a great deal of advantages in classifying HRS image.

In remote sensing, pansharpening refers to the technique that combines the complementary spectral and spatial resolution characteristics of a multispectral image and a panchromatic image, with the objective to generate a high-resolution color image. This paper presents a new pansharpening method based on the minimization of a variant of total variation. We consider the fusion problem as the colorization of each pixel in the panchromatic image. A new term concerning the gradient of the panchromatic image is introduced in the functional of total variation so as to preserve edges. Experimental results on IKONOS satellite images demonstrate the effectiveness of the proposed method.

This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored system property. The main idea is to divide the decision into several partial decisions and then to aggregate these to get the final one. The adaptation is the result of the aggregation process which aims at selecting and summarizing the partial decisions which are based on coherent information according to learnt models. The suggested method is presented. Experiments on a two-class image segmentation problem are performed and analyzed. The results assessed that the suggested method is more robust when an abrupt change occurs and is able to select efficiently the partial decision makers. This approach opens a wide field of applications and results are very encouraging.

This paper presents a benchmarking system for the evaluation of robust mesh watermarking methods. The proposed benchmark has three different components: a ''standard'' mesh model collection, a software tool and two application-oriented evaluation protocols. The software tool integrates both geometric and perceptual measurements of the distortion induced by watermark embedding, and also the implementation of a variety of attacks on watermarked meshes. The two evaluation protocols define the main steps to follow when conducting the evaluation experiments. The efficiency of the benchmark is demonstrated through the evaluation and comparison of two recent robust algorithms.

In this paper, we present a new feature subset selection method that intends to optimize or preserve the performances of a decisional system in case of nonstationary perturbations or loss of information. A two-step process is proposed. First, multiple classifiers are created based on random subspace method, and an initial decision is obtained by combining all the classifiers according to a weighted voting rule. Then, we classify anew all the observations with a subset of these classifiers, chosen in function of the quality of their related feature subspaces. To illustrate this approach, the two-class textured image segmentation problem is considered. Our attention is focused on trying to determine the optimum feature subsets in order to improve the classification accuracy at the borders between two textures. Experimental results demonstrate the effectiveness of the proposed approach.