Runsheng Wang

Dynamic Learning of SMLR for Feature Selection and Classification of Hyperspectral Data

Feature selection is an important task in the analysis of hyperspectral data. Recently developed methods for learning sparse classifiers, which combine the automatic feature selection and classifier design, established themselves among the state of the art in the literature of machine learning. In this letter, the sparse multinomial logistic regression (SMLR) is introduced into the community of remote sensing and is utilized for the feature selection in the classification of hyperspectral data. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when the SMLR selects a small feature subset, we develop a dynamic learning framework to train the SMLR. Experimental results attest to the effectiveness of the proposed method.

Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Data

This paper proposes to improve the classification accuracy of hyperspectral data with support vector machines (SVMs) by using stacked generalization (stacking) as well as the complementary information of magnitude and shape feature spaces. Stacking is a method to combine multiple classifiers by learning a meta-level (or level-1) classifier from the outputs of base-level (or level-0) classifiers (estimated via cross-validation). In the processing of hyperspectral data, magnitude features are the radiance values at different sensor bands, whereas shape features are the differences in direction rather than the magnitude of the spectral signatures. In particular, the proposed method is as follows: (1) SVMs trained in magnitude and shape feature spaces are adopted as level-0 classifiers (termed as level-0 SVMs); (2) outputs (decision values) of the level-0 SVMs are used as inputs (termed as meta-level features) of level-1 classifier, since the decision values contain much more information than class labels; (3) level-1 classifier adopts SVMs (level-1 SVMs) trained in the meta-level feature space. In addition, we also discuss the possibility of reducing the number of level-0 SVMs by meta-level feature selection and present one simple solution. Experiments on a benchmark hyperspectral data set demonstrate that our method significantly outperforms the methods with the single feature space and other combining methods, namely, simple voting, absolute maximum decision value, and stacking with class labels.

Classified road detection from satellite images based on perceptual organization

Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub‐processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob‐like primitive and line‐like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre‐main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability.

Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery

Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures.

Modeling and Classifying Hyperspectral Imagery by CRFs With Sparse Higher Order Potentials

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images.

Combining Support Vector Machines With a Pairwise Decision Tree

To address the multiclass classification problem of hyperspectral data, a new method called pairwise decision tree of support vector machines (PDTSVM) is proposed. For an N -class problem, after training N(N - 1)/2 binary support vector machines (SVMs) for each pair of information class, PDTSVM only requires N - 1 binary SVMs for one classification. Based on the separability estimated by the geometric margin between two classes, binary SVMs are recursively selected by using a fast sequential forward selection. Each binary SVM is used to exclude the less-similar class. PDTSVM eliminates the wrong votes of the one-against-one method. It also has much fewer layers than other tree-based methods, which decreases accumulated errors. Tested with an 11-class problem, the results demonstrate the effectiveness of our method.

Letters: Adaptive binary tree for fast SVM multiclass classification

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.

Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery

Feature selection is an important task in hyperspectral data analysis. This paper presents a sparse conditional random field (SCRF) model to select relevant features for the classification of hyperspectral images and, meanwhile, to exploit the contextual information in the form of spatial dependences in the images. The sparsity arises from the use of a Laplacian prior on the CRF parameters, which encourages the parameter estimates to be either significantly large or exactly zero. To joint the feature selection and classifier design, this paper develops an efficient sparse training method, which divides the training of SCRF into the sparse trainings of two simpler classifiers. Experiments on the real-world hyperspectral image attest to the accuracy, sparsity, and efficiency of the proposed model.

Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification

Gaussian process (GP) classifiers represent a powerful and interesting theoretical framework for the Bayesian classification of hyperspectral images. However, the collection of labeled samples is time consuming and costly for hyperspectral data, and the training samples available are often not enough for an adequate learning of the GP classifier. Moreover, the computational cost of performing inference using GP classifiers scales cubically with the size of the training set. To address the limitations of GP classifiers for hyperspectral image classification, reducing the label cost and keeping the training set in a moderate size, this paper introduces an active learning (AL) strategy to collect the most informative training samples for manual labeling. First, we propose three new AL heuristics based on the probabilistic output of GP classifiers aimed at actively selecting the most uncertain and confusing candidate samples from the unlabeled data. Moreover, we develop an incremental model updating scheme to avoid the repeated training of the GP classifiers during the AL process. The proposed approaches are tested on the classification of two realworld hyperspectral data. Comparison with random sampling method reveals a better accuracy gain and faster convergence with the number of queries, and comparison with recent active learning approaches shows a competitive performance. Experimental results also verified the efficiency of the incremental model updating scheme.

Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image

With the complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing images. In the literature of land-cover analysis, many algorithms utilize the features with structural information to characterize urban areas. Typically, these are more successful on some types of imagery than others, since they usually use only one kind or a few kinds of structural information. On the other hand, since levels of development in neighboring areas are not statistically independent, the multiple features (encoding the multilevel structural information) of each site in urban area depend on that of neighboring sites. In this paper, a new-come discriminative model, i.e., conditional random field (CRF), is introduced to learn the dependencies and fuse the multilevel structural information to obtain the essential detection. To meet the higher needs of some users, we introduce a two-component-based Markov random field model and show how to integrate it tightly with CRF model to refine the results from essential detection. Experiments on a wide range of images show that our algorithms are competitive with recent results in urban area detection