Shuiping Gou

Parallel Sparse Spectral Clustering for SAR Image Segmentation

A novel parallel spectral clustering approach is proposed by exploiting the distributed computing in MATLAB for SAR image segmentation quickly and accurately. For large-scale data applications, most existing spectral clustering algorithms suffer from the bottleneck problems of high computational complexity and large memory use. And in the absence of advanced hardware and software equipments with only the loosely coupled computer resources accessible, the framework of MATLAB Parallel Computing-based sparse spectral clustering is constructed in this paper. In the proposed frame, we use a distributed parallel computing model to accelerate computation, where each partition of data instances is assigned to different processor nodes for the similarity matrix calculation in spectral clustering. Further, by the construction of exact t-nearest neighbor sparse symmetric similarity matrix, the sparseness technique is employed to alleviate the storage stress. Besides, the problems of how to choose the number of nearest neighbors and the scaling parameter are also discussed. The segmentation results on artificial synthesis texture images and SAR images show that the proposed parallel algorithm can effectively handle large-size segmentation cases. Meanwhile, it can obtain better segmentation results compared with Nyström approximation spectral clustering and k-means clustering algorithm.

Weighted classifier ensemble based on quadratic form

Diversity and accuracy are the two key factors that decide the ensemble generalization error. Constructing a good ensemble method by balancing these two factors is difficult, because increasing diversity is at the cost of reducing accuracy normally. In order to improve the performance of an ensemble while avoiding the difficulty derived of balancing diversity and accuracy, we propose a novel method that weights each classifier in the ensemble by maximizing three different quadratic forms. In this paper, the optimal weight of individual classifiers is obtained by minimizing the ensemble error, rather than analyzing diversity and accuracy. Since it is difficult to minimize the general form of the ensemble error directly, we approximate the error in an objective function subject to two constraints ( ? w i = 1 and - 1 < w i < 1 ). Particularly, we introduce an error term with a weight vector w0, and subtract this error with the quadratic form to obtain our approximated error. This subtraction makes minimizing the approximation form equivalent to maximizing the original quadratic form. Theoretical analysis finds that when the value of the quadratic form is maximized, the error of an ensemble system with the corresponding optimal weight w* will be smallest, especially compared with the ensemble with w0. Finally, we demonstrate improved classification performance from the experimental results of an artificial dataset, UCI datasets and PolSAR image data. A new weighted classifier ensemble method is proposed.An approximation form of the ensemble error is introduced.An optimal weight vector is sought based on minimizing the approximation form.It is converted into maximizing quadratic forms by invoking a known weight vector.The larger the value of the quadratic form is, the lower the ensemble error is.

Multi-elitist immune clonal quantum clustering algorithm

The quantum clustering (QC) algorithm suffers from the issues of getting stuck in local extremes and computational bottleneck when handling large-size image segmentation. By embedding a potential evolution formula into affinity function calculation of multi-elitist immune clonal optimization, and updating the cluster center based on the distance matrix, the multi-elitist immune clonal quantum clustering algorithm (ME-ICQC) is proposed in this paper. In the proposed framework, elitist population is composed of the individuals with high affinity, which is considered to play dominant roles in the evolutionary process. It can help to find the global optimal solution or near-optimal solution for most tested tasks. The diversity of population can be well maintained by general subgroup evolution of ME-ICQC. These different functions are implemented by the dissimilar mutation strategies or crossover operators. The bi-group exchanges the information of excellence antibodies using the hypercube co-evolution operation. Compared with existing algorithms, the ME-ICQC achieves an improved clustering accuracy with more stable convergence, but it is not significantly better than other optimization techniques combined with QC. Also, the experimental results also show that our algorithm performs well on multi-class, parameters-sensitive and large-size datasets.

Coastal Zone Classification With Fully Polarimetric SAR Imagery

Classifying different types of land cover in coastal zones using synthetic aperture radar (SAR) imagery is a challenge due to the fact that many types of coastal zone have similar backscattering characteristics. In this letter, we propose an unsupervised method based on a three-channel joint sparse representation (SR) classification with fully polarimetric SAR (PolSAR) data. The proposed method utilizes both texture and polarimetric feature information extracted from the HH, HV, and VV channels of a SAR image. The texture features are extracted by applying a wavelet transform to a SAR image, and then sparsely represented based on the correlation among the three channels. The polarimetric features, i.e., the scattering entropy and scattering angle from the H/α model, are also sparsely represented. A joint SR algorithm using both texture and polarimetric features is constructed to establish target dictionaries. An orthogonal matching pursuit algorithm is then used to calculate sparse coefficients. Hybrid coefficients are inputted to the kernel support vector machine for a fully PolSAR image classification. We applied the proposed algorithm to an Advanced Land Observing Satellite-2 L-band SAR image acquired in the Yellow River Delta, China. The classified land types are validated against the official survey map. The algorithm performs well in distinguishing six coastal land-use types. A comparison study is also conducted to show that proposed algorithm outperforms two commonly used classification methods.

Quantum-Inspired immune clonal algorithm for multiuser detection in DS-CDMA systems

This paper proposes a new immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QICA is also characterized by the representation of the antibody (individual), the evaluation function, and the population dynamics. However, in QICA, an antibody is proliferated and divided into a subpopulation. Antibodies in a subpopulation are represented by multi-state gene quantum bits. For the novel representation, we put forward the quantum mutation operator which is used at the inner subpopulation to accelerate the convergence. Finally, QICA is applied to a practical case, the multiuser detection in DS-CDMA systems, with a satisfactory result.

Remote Sensing Image Super-Resolution Reconstruction Based on Nonlocal Pairwise Dictionaries and Double Regularization

A nonlocal pairwise dictionary learning (NPDL) model that includes an estimated dictionary and a residual dictionary is applied to remote sensing image super-resolution (SR) reconstruction in this paper. The dictionary pair is trained from some low-resolution (LR) remote sensing images to deal with the lack of high-resolution component in remote sensing images. The reconstructed image has been shown to retain the structural information of the given LR image itself. Moreover, the local and nonlocal (NL) priors are used for image SR to enhance robustness of the pairwise dictionary. Improved NL self-similarity and local kernel constraint regularization terms are introduced to the image optimization process. Using this, the photometric, geometric, and feature information of the given LR image can be taken into consideration to improve the quality of reconstruction. Simulation results show that the proposed algorithm can achieve better visual effects and the average peak signal-to-noise ratio (PSNR) is improved by approximately 0.5 db compared with the state-of-the-art image SR methods.

Image Segmentation Based on Fussing Multi-feature and Spatial Spectral Clustering

A new method for image feature extraction and segmentation is proposed in this paper. Abundant contour feature information of the image is expressed by contourlet transform while texture feature of the image is described by wavelet transform and Gray Level Co-occurrence Matrix (GLCM). The three type feature information compose feature matrix. The presented method describes different image information using different characterization transform and keeps well useful original image information. Then we select spectral mapping to simply the feature matrix and gain distributed datasets. And the images are segmented by fuzzy clustering algorithm with spatial constraints, which can improve the robustness of the proposed method to the images containing noise. Simulation results of the texture images and Synthetic Aperture Radar (SAR) images show the proposed method had higher accuracy compared with traditional spectral clustering.

Polarimetric SAR Feature Extraction With Neighborhood Preservation-Based Deep Learning

As an advanced nonlinear technique, deep learning, which is based on deep neural networks (DNNs), has attracted considerable attentions. In this paper, we propose a novel neighborhood preserved deep neural network (NPDNN) for polarimetric synthetic aperture radar feature extraction and classification. The spatial relation between pixels is exploited by a jointly weighting strategy. Not only the spatial neighbors but also the pixels in the same superpixel are utilized to weight each pixel. This strategy maintains the spatial dependence leading to superior homogeneity of the terrains without extra computational memory. Moreover, a few labeled samples and their nearest neighbors are employed to train the multilayer NPDNN, which preserves the local structure and reduces the number of labeled samples for classification. Experimental results on synthesized and real PolSAR data show that the proposed NPDNN can improve the classification accuracy compared with state-of-the-art DNNs despite a few input samples.

Semisupervised Feature Extraction With Neighborhood Constraints for Polarimetric SAR Classification

The supervised feature extraction methods have a relative high performance since the discriminating information of classes is introduced from large quantities of labeled samples. However, it is labor intensive to obtain labeled samples for terrain classification. In this paper, in order to reduce the cost of labeled samples, a novel semisupervised algorithm with neighborhood constraints (SNC) is proposed for polarimetric synthetic aperture radar (PolSAR) feature extraction and terrain classification. A number of PolSAR features of each pixel and its neighbors are used to construct a spatial group, which can represent the central pixel and weaken the influence of speckle noise. Then, with the class information from a few of pixels and the neighborhood constraints, an objective function is designed for the estimation of a nonlinear low-dimensional space. Finally, the spatial groups in the original high-dimensional space are projected to this low-dimensional space, and a low-dimensional feature set is obtained. The redundancy among the features is reduced. Additionally, unlike the conventional semisupervised algorithms, because the local spatial relation of PolSAR image is utilized, the extracted features not only are discriminating but also preserve the structure of the PolSAR data, which can enhance the classification accuracy. Experiments using the extracted features for classification are performed on both the synthesized PolSAR and real PolSAR data which are from the AIRSAR, RADARSAT-2, and EMISAR. Quantitative results indicate that SNC improves the separability of features and is superior to state-of-the-art feature extraction algorithms with a few labeled pixels.

Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.