Lianru Gao

Chinese Academy of Sciences, Peping, Beijing, China

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Publications (55)63.31 Total impact

  • Liwei Li · Bing Zhang · Wei Li · Lianru Gao

    No preview · Article · Feb 2016 · Pattern Recognition Letters
  • Qiandong Guo · Ruiliang Pu · Lianru Gao · Bing Zhang
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    ABSTRACT: Anomaly detection is an active research topic in hyperspectral remote sensing and has been applied in many areas including environmental monitoring, urban survey, mineral mapping, and national security. Usually, it makes a detection decision without any prior target information or background information. Several anomaly detection algorithms (e.g. Reed-Xiaoli detector, blocked adaptive computationally efficient outlier nominator and random-selection-based anomaly detector) have been developed, which rely on estimating background information only from a hyperspectral image without considering target information in making a detection decision. These methods may be efficient in general but sometimes with high false alarm rate (FAR). In order to reduce FAR, this study proposes a novel method that incorporates both background and target information, derived from the hyperspectral imagery, into anomaly detection algorithms. The target information is helpful to detect anomalies as outliers. With a scene of real airborne visible infrared imaging spectrometer data, the experimental results demonstrate that the proposed method has produced better detection results and higher time efficiency compared with those using the traditional algorithms that only consider background information.
    No preview · Article · Jan 2016 · Remote Sensing Letters
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    Xu Sun · Lina Yang · Bing Zhang · Lianru Gao · Jianwei Gao
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    ABSTRACT: Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM) constitutes a geometric approach that is commonly used for this purpose. This paper introduces the use of artificial bee colony (ABC) algorithms for spectral unmixing. First, the objective function of the external minimum volume model is improved to enhance the robustness of the results, and then, the ABC-based endmember extraction process is presented. Depending on the characteristics of the objective function, two algorithms, Artificial Bee Colony Endmember Extraction-RMSE (ABCEE-R) and ABCEE-Volume (ABCEE-V) are proposed. Finally, two sets of experiment using synthetic data and one set of experiments using a real hyperspectral image are reported. Comparative experiments reveal that ABCEE-R and ABCEE-V can achieve better endmember extraction results than other algorithms when processing data with a low signal-to-noise ratio (SNR). ABCEE-R does not require high accuracy in the number of endmembers, and it can always obtain the result with the best root mean square error (RMSE); when the number of endmembers extracted and the true number of endmembers does not match, the RMSE of the ABCEE-V results is usually not as good as that of ABCEE-R, but the endmembers extracted using the former algorithm are closer to the true endmembers.
    Preview · Article · Dec 2015 · Remote Sensing
  • Xu Sun · Lina Yang · Lianru Gao · Bing Zhang · Shanshan Li · Jun Li
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    ABSTRACT: Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).
    No preview · Article · Oct 2015 · Journal of Applied Remote Sensing
  • Xiaoxia Sun · Liwei Li · Bing Zhang · Dongmei Chen · Lianru Gao
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    ABSTRACT: In this article, a new strategy is proposed for soft urban land-cover extraction based on mixed training data and Support Vector Machines (SVMs). The strategy is applied to soft classification of urban water cover using Landsat 8 multispectral data in Beijing. The results are validated with extensive manual mapping data and are compared with the results derived from a well-trained SVM and the linear spectral unmixing method using only pure samples. Our experimental results indicate that the proposed strategy works effectively in extracting the water cover in the urban image. The results are better than those obtained by SVM and linear spectral unmixing with only pure samples. Our findings demonstrate that the combination of image-based mixed training data and SVM enhances the strengths of kernel-based approaches for soft mapping urban land cover from imaging data. Therefore, the proposed workflow constitutes a new flexible and extendable approach to soft mapping urban land cover. Future work includes the construction of a comprehensive image-based spectral library for urban areas, and the testing of the proposed strategy on additional land-cover types in urban areas such as grass, tree, and bare soil.
    No preview · Article · Jul 2015 · International Journal of Remote Sensing
  • Lianru Gao · Bin Yang · Qian Du · Bing Zhang
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    ABSTRACT: Supervised target detection and anomaly detection are widely used in various applications, depending upon the availability of target spectral signature. Basically, they are based on a similar linear process, which makes them highly correlated. In this paper, we propose a novel adjusted spectral matched filter (ASMF) for hyperspectral target detection, which aims to effectively improve target detection performance with anomaly detection output. Specifically, a typical case is presented by using the Reed-Xiaoli (RX) anomaly detector to adjust the output of supervised constrained energy minimization (CEM) detector. The adjustment is appropriately controlled by a weighting parameter in different detection scenarios. Experiments were implemented by using both synthetic and real hyperspectral datasets. Compared to the traditional single detection method (e.g., CEM), the experimental results demonstrate that the proposed ASMF can effectively improve its performance by utilizing the result from an anomaly detector (e.g., RX), particularly in situations with a complex background or strong anomalies.
    No preview · Article · Jun 2015 · Remote Sensing
  • Bin Yang · Minhua Yang · Antonio Plaza · Lianru Gao · Bing Zhang
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    ABSTRACT: Target and anomaly detection are important techniques for remotely sensed hyperspectral data interpretation. Due to the high dimensionality of hyperspectral data and the large computational complexity associated to processing algorithms, developing fast techniques for target and anomaly detection has received considerable attention in recent years. Although several high-performance architectures have been evaluated for this purpose, field programmable gate arrays (FPGAs) offer the possibility of onboard hyperspectral data processing with low-power consumption, reconfigurability and radiation tolerance, which make FPGAs a relevant platform for hyperspectral processing. In this paper, we develop a novel FPGA-based technique for efficient target detection in hyperspectral images. The proposed method uses a streaming background statistics (SBS) approach for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms, which are widely used techniques for target and anomaly detection, respectively. Specifically, these two algorithms are implemented in streaming fashion on FPGAs. Most importantly, we present a dual mode that implements a flexible datapath to decide in real time which one among these two algorithms should be used, thus allowing for the dynamic adaptation of the hardware to either target detection or anomaly detection scenarios. Our experiments, conducted with several well-known hyperspectral scenes, indicate the effectiveness of the proposed implementations.
    No preview · Article · Jun 2015 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Lina Zhuang · Bing Zhang · Lianru Gao · Jun Li · Antonio Plaza
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    ABSTRACT: The normal compositional model (NCM) has been introduced to characterize mixed pixels in hyperspectral images, particularly when endmember variability needs to be considered in the unmixing process. Each pixel is modeled as a linear combination of endmembers, which are treated as Gaussian random variables in order to capture such spectral variability. Since the combination coefficients (i.e., abundances) and the endmembers are unknown variables at the same time in the NCM, the parameter estimation is more difficult in comparison with conventional approaches. In order to address this issue, we propose a new Bayesian method, termed normal endmember spectral unmixing (NESU), for improved parameter estimation in this context. It considers the endmembers as known variables (resulting from the extraction of endmember bundles), then performs optimal estimations of the remaining unknown parameters, i.e., the abundances, using Bayesian inference. The particle swarm optimization (PSO) technique is adopted to estimate the optimal values of abundances according to their posterior probabilities. The performance of the proposed algorithm is evaluated using both synthetic and real hyperspectral data. The obtained results demonstrate that the proposed method leads to significant improvements in terms of unmixing accuracies.
    No preview · Article · Jun 2015 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Yuanfeng Wu · Jun Li · Lianru Gao · Xuemin Tan · Bing Zhang
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    ABSTRACT: We propose a commodity graphics processing units (GPUs)-based massively parallel efficient computation for spectral-spatial classification of hyperspectral images. The spectralspatial classification framework is based on the marginal probability distribution which uses all of the information in the hyperspectral data. In this framework, first, the posterior class probability is modeled with discriminative random field in which the association potential is linked with a multinomial logistic regression (MLR) classifier and the interaction potential modeling the spatial information is linked to a Markov random field multilevel logistic (MLL) prior. Second, the maximizers of the posterior marginals are computed via the loopy belief propagation (LBP) method. In addition, the regressors of the multinominal logistic regression classifier are inferred by the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. Although the spectral-spatial classification framework exhibited state-of-the-art accuracy performance with regard to similar approaches, its computational complexity is very high. We take advantage of the massively parallel computing capability of NVIDIATesla C2075 with the compute unified device architecture including a set of GPU-accelerated linear algebra libraries (CULA) to dramatically improve the computation speed of this hyperspectral image classification framework. The shared memory and the asynchronous transfer techniques are also used for further computationally efficient optimization. Real hyperspectral data sets collected by the National Aeronautics and Space Administration's airborne visible infrared imaging spectrometer and the reflective optics system imaging spectrometer system are used for effectiveness evaluation. The results show that we achieved a speedup of 92-fold on LORSAL, 69-fold on MLR, 127-fold on MLL, 160-fold on LBP, and 73-fold on the whole spectral-spatial classification framework as compared with its single-core central processing unit counterpart, respectively.
    No preview · Article · Mar 2015 · Journal of Applied Remote Sensing
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    ABSTRACT: Hyperspectral image classification has been a very active area of research in recent years. It faces challenges related with the high dimensionality of the data and the limited availability of training samples. In order to address these issues, subspace-based approaches have been developed to reduce the dimensionality of the input space in order to better exploit the (limited) training samples available. An example of this strategy is a recently developed subspace-projection-based multinomial logistic regression technique able to characterize mixed pixels, which are also an important concern in the analysis of hyperspectral data. In this letter, we extend the subspace-projection-based concept to support vector machines (SVMs), a very popular technique for remote sensing image classification. For that purpose, we construct the SVM nonlinear functions using the subspaces associated to each class. The resulting approach, called SVM $sub$, is experimentally validated using a real hyperspectral data set collected using the National Aeronautics and Space Administration's Airborne Visible/Infrared Imaging Spectrometer. The obtained results indicate that the proposed algorithm exhibits good performance in the presence of very limited training samples.
    No preview · Article · Feb 2015 · IEEE Geoscience and Remote Sensing Letters
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    ABSTRACT: Spectral unmixing is an important technique in hyperspectral image exploitation. It comprises the extraction of a set of pure spectral signatures (called endmembers in hyperspectral jargon) and their corresponding fractional abundances in each pixel of the scene. Over the last few years, many approaches have been proposed to automatically extract endmembers, which is a critical step of the spectral unmixing chain. Recently, ant colony optimization (ACO) techniques have reformulated the endmember extraction issue as a combinatorial optimization problem. Due to the huge computation load involved, how to provide suitable candidate endmembers for ACO is particularly important, but this aspect has never been discussed before in the literature. In this paper, we illustrate the capacity of ACO techniques for integrating the results obtained by different endmember extraction algorithms. Our experimental results, conducted using several state-of-the-art endmember extraction approaches using both simulated and a real hyperspectral scene (cuprite), indicate that the proposed ACO-based strategy can provide endmembers which are robust against noise and outliers.
    No preview · Article · Dec 2014 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Bing Zhang · Lina Zhuang · Lianru Gao · Wenfei Luo · Qiong Ran · Qian Du
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    ABSTRACT: A new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization-expectation maximization (PSO-EM) algorithm, a “winner-take-all” version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods.
    No preview · Article · Dec 2014 · IEEE Transactions on Geoscience and Remote Sensing
  • Lianru Gao · Lina Zhuang · Yuanfeng Wu · Xu Sun · Bing Zhang
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    ABSTRACT: Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.
    No preview · Article · Nov 2014 · Soft Computing
  • Lianru Gao · Qiandong Guo · Antonio Plaza · Jun Li · Bing Zhang
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    ABSTRACT: Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
    No preview · Article · Nov 2014 · Journal of Applied Remote Sensing
  • Source
    Li Ni · Lianru Gao · Shanshan Li · Jun Li · Bing Zhang
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    ABSTRACT: This paper proposes an edge-constrained Markov random field (EC-MRF) method for accurate land cover classification over urban areas using hyperspectral image and LiDAR data. EC-MRF adopts a probabilistic support vector machine for pixel-wise classification of hyperspectral and LiDAR data, while MRF performs as a postprocessing regularizer for spatial smoothness. LiDAR data improve both pixel-wise classification and postprocessing result during an EC-MRF procedure. A variable weighting coefficient, constrained by a combined edge extracted from both hyperspectral and LiDAR data, is introduced for the MRF regularizer to avoid oversmoothness and to preserve class boundaries. The EC-MRF approach is evaluated using synthetic and real data, and results indicate that it is more effective than four similar advanced methods for the classification of hyperspectral and LiDAR data. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
    Full-text · Article · Oct 2014 · Journal of Applied Remote Sensing
  • Bin Yang · Minhua Yang · Lianru Gao · Bing Zhang
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    ABSTRACT: Target detection is an important topic for extracting information from hyperspectral imagery. Due to the high computational complexity, developing fast target detection algorithm has received considerable interest, specifically to take advantage of field-programmable gate array (FPGA) architecture in hardware implementation. In this paper, a novel streaming background statistics (SBS) method is used for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms which are the benchmarks of signal-based target detection and anomaly detection, respectively. As a purpose, these two classic algorithms can be easily calculated in a streaming update way. Finally, we present a dual mode FPGA implementation in which, two different algorithms are arranged for different detection requirements within a real-time processing.
    No preview · Article · Oct 2014
  • Liwei Li · Bing Zhang · Lianru Gao · Yuanfeng Wu
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    ABSTRACT: We propose a new method for spectral feature extraction based on Orthogonal Polynomial Function (OPF) fitting. Given a spectral signature, it is firstly divided into spectral segments by a splitting strategy. All segments are fitted by using OPF respectively. The features of input spectrum are selected from the fitting coefficients of all segments. 10 laboratory spectra of various materials are selected to validate the ability of the proposed method. The results show that our method can efficiently mine geometric structural information of spectral signatures, and compress them into a few parameters. These parameters can be used to sparsely represent the input spectra and also well discriminate different spectral signatures. The proposed method is more powerful than the inverse Gaussian function model as it can not only will fit the red-edge spectral segment but also can fit other types of spectral curves. Also, the extracted features are slightly better than the original bands at the ability of discrimination in terms of RSDPW in Euclidean space while largely reduce the number of features. Overall, the proposed method has promising prospects in hyperspectral data analysis.
    No preview · Article · Sep 2014
  • Yuanfeng Wu · Lianru Gao · Bing Zhang · Haina Zhao · Jun Li
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    ABSTRACT: We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.
    No preview · Article · Aug 2014 · Journal of Applied Remote Sensing
  • Jianwei Gao · Qian Du · Lianru Gao · Xu Sun · Bing Zhang
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    ABSTRACT: Band selection (BS), which selects a subset of original bands that contain the most useful information about objects, is an important technique to reduce the dimensionality of hyperspectral data. Dimensionality reduction before hyperspectral data classification can reduce redundancy information and even improve classification accuracy. We propose BS algorithms based on an ant colony optimization (ACO) in conjunction with objective functions such as the supervised Jeffries-Matusita distance and unsupervised simplex volume. Moreover, we propose to use a small number of selected pixels for BS in order to reduce computational cost in the unsupervised BS. In this experiment, the proposed algorithms were applied to three airborne hyperspectral datasets including urban scenes, and the results demonstrated that the ACObased BS could find a better combination of bands than the widely used sequential forward search-based BS. It was acceptable to use a few pixels to achieve comparable BS performance with our method.
    No preview · Article · Aug 2014 · Journal of Applied Remote Sensing
  • Qingting Li · Bing Zhang · Lianru Gao · Linlin Lu · Quanjun Jiao
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    ABSTRACT: Imaging spectroscopic technique has been used for the mineral and rock geological mapping and alteration information extraction successfully with many reasonable results, but it is mainly used in arid and semi-arid land with sparse vegetation covering. In the case of the dense vegetation covering, the outcrop of the altered rocks is small and distributes sparsely, the altered rocks is difficult to be identified directly. The target detection technique using imaging spectroscopic data should be introduced to the extraction of small geological targets under dense vegetation covering area. In the paper, we take Ding-Ma gold deposit as the study area which located in Zhenan country, Shanxi province, China. Some target detection algorithms which are appropriate to the small geological target detection are introduced based on the study of the principle of the algorithms. At last, the small altered rock targets under the covering of vegetation in forest are detected and discriminated using imaging spectroscopy data with the methods of spectral angle map(SAM), Orthogonal Subspace Projection(OSP), Constrained Energy Minimization(CEM), Adaptive Coherence/Cosine Estimator(ACE), Adaptive Matched Filter(AMF), Elliptically Contoured Distributions(ECD). The detection results are reasonable and indicate the ability of target detection algorithms for geological target detection in the forest area.
    No preview · Conference Paper · Jul 2014

Publication Stats

179 Citations
63.31 Total Impact Points

Institutions

  • 2007-2016
    • Chinese Academy of Sciences
      • • Institute of Remote Sensing and Digital Earth
      • • Center for Earth Observation and Digital Earth
      • • Institute of Remote Sensing Applications
      Peping, Beijing, China
  • 2012
    • Center for Earth Observation and Digital Earth (CEODE)
      Peping, Beijing, China