Zhaohui Xue

Spectral–Spatial Classification of Hyperspectral Data via Morphological Component Analysis-Based Image Separation

This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based image separation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen image partitions. Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratorys Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed scheme provides better performance when compared with other widely used methods.

Harmonic Analysis for Hyperspectral Image Classification Integrated With PSO Optimized SVM

A novel hyperspectral image classification approach named as HA-PSO-SVM is proposed by integrating the harmonic analysis (HA), particle swarm optimization (PSO), and support vector machine (SVM). In the combined method, HA is first proposed to transform the pixels from spectral domain into frequency domain expressed by amplitude, phase and residual, yielding more functional and discriminative features for classification purpose. In this step, the original pixel vector can also be reconstructed. Then, PSO is adapted to optimize the penalty parameter C and the kernel parameter γ for SVM, which leads to improved classification performance. Finally, the extracted features are classified with the optimized model. The experimental results with three hyperspectral data sets collected by the airborne visible infrared imaging spectrometer (AVIRIS) and the reflective optics spectrographic imaging system (ROSIS) indicate that the proposed method provides improved classification performance compared with some related techniques in terms of both the classification accuracy and the computational time.

Spatiotemporal Pattern of PM 2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998–2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran’s I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.

Phenology-Driven Land Cover Classification and Trend Analysis Based on Long-term Remote Sensing Image Series

The objective of this study is to classify the land cover types and analyze the land cover trend by incorporating phenological variability throughout a range of natural ecosystems using time-series remotely sensed images. First, a breaks for additive seasonal and trend (BFAST) approach is used to extract the phenology information from the time series. Second, a dynamic time warping (DTW) approach is adopted to screen the additional interpreted samples used for training. Third, some ensemble learning classifiers and the support vector machine (SVM) are performed to classify the land cover types based on the BFAST-derived phenology components. Finally, some inter-annual phenological markers are extracted to facilitate the land cover trend analysis by taking the climate fluctuations and anthropogenic forcing into consideration. The experimental results with normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data collected by the Moderate Resolution Imaging Spectrometer (MODIS) indicate that the classification accuracy is significantly improved by using the phenology information and the phenological markers can lead to a better understanding of the regional land cover change.

Learning Discriminative Sparse Representations for Hyperspectral Image Classification

In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.

Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification

Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.

Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Kernel-based methods and ensemble learning are two important paradigms for the classification of hyperspectral remote sensing images. However, they were developed in parallel with different principles. In this paper, we aim to combine the advantages of kernel and ensemble methods by proposing a kernel supervised ensemble classification method. In particular, the proposed method, namely RoF-KOPLS, combines the merits of ensemble feature learning (i.e., Rotation Forest (RoF)) and kernel supervised learning (i.e., Kernel Orthonormalized Partial Least Square (KOPLS)). In particular, the feature space is randomly split into K disjoint subspace and KOPLS is applied to each subspace to produce the new features set for the training of decision tree classifier. The final classification result is assigned to the corresponding class by the majority voting rule. Experimental results on two hyperspectral airborne images demonstrated that RoF-KOPLS with radial basis function (RBF) kernel yields the best classification accuracies due to the ability of improving the accuracies of base classifiers and the diversity within the ensemble, especially for the very limited training set. Furthermore, our proposed method is insensitive to the number of subsets.

Sparse Graph Regularization for Hyperspectral Remote Sensing Image Classification

Regularization has appeared explicitly in hyperspectral image (HSI) classification community, which serves as a promising paradigm for leveraging labeled and unlabeled information, computer’s automation and user’s interaction, spectral and spatial information, and so on. Graph-based regularization is capable of modeling the nonlinear structures embedded in high-dimensional space, with the great potential for HSI classification. However, traditional methods exhibit low capacity when facing noisy and large-scale data, thus posing a big challenge for their successful use in this community. In this paper, we present two novel sparse graph regularization methods, SGR and SGR with total variation (TV-SGR). In SGR, the labels of large unknown data are propagated based on the fraction matrix and the prediction function, where the fraction matrix is obtained using an effective sparse representation (SR) algorithm with respect to the dictionary, and the prediction function is estimated by optimizing a typical graph-based regularization problem. In contrast, TV-SGR is an extension of SGR by considering spatial information modeled by total variation in SR. Propagating the prediction function from dictionary to large unknown data using the fraction matrix is the essence of the paradigm. SGR and TV-SGR can be equipped with semisupervised learning, active learning, and spectral–spatial classification with large flexibility. The experimental results with two popular hyperspectral data sets indicate that the proposed methods outperform some state-of-the-art approaches in terms of computational efficacy, classification accuracy, and robustness to noise.

Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective

This paper presents a novel semi-supervised joint dictionary learning (S2JDL) algorithm for hyperspectral image classification. The algorithm jointly minimizes the reconstruction and classification error by optimizing a semi-supervised dictionary learning problem with a unified objective loss function. To this end, we construct a semi-supervised objective loss function which combines the reconstruction term from unlabeled samples and the reconstruction–discrimination term from labeled samples to leverage the unsupervised and supervised information. In addition, a soft-max loss is used to build the reconstruction–discrimination term. In the training phase, we randomly select the unlabeled samples and loop through the labeled samples to comprise the training pairs, and the first-order stochastic gradient descents are calculated to simultaneously update the dictionary and classifier by feeding the training pairs into the objective loss function. The experimental results with three popular hyperspectral datasets indicate that the proposed algorithm outperforms the other related methods.

Annual Landsat analysis of urban growth of Nanjing City from 1980 to 2013

The spatial-temporal evolution of impervious surface expansion is essential for an better and powerful understanding of the urbanization process and its impact on environment and climate change. In this paper, a Classification and Regression Tree (CART) algorithm which is based on a supervised classification using ENVIs native Decision Tree tool is applied for retrieving continuous, long-term expansion of impervious surface using annual medium-resolution satellite images. The main goal of this paper is to achieve annual, long-term impervious surface maps from a corresponding time series of images collected by multiple sensors. Hence, three decades of medium-resolution satellite data of Nanjing city from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and Operational Land Imager (OLI) is used to extract annual, long-term impervious surface maps for quantitatively evaluating the process of regional urbanization. Results show that the areas of annual impervious surface are all overestimated but the trend of growth is consistent with statistical data. The overall accuracies vary in different years from 86.2% to 94.1% and the commission and omission errors for the impervious type are about 12.6% and 6.2%.