Lianru Gao

Chinese Academy of Sciences, Peping, Beijing, China

Are you Lianru Gao?

Claim your profile

Publications (48)55.91 Total impact

  • Xiaoxia Sun · Liwei Li · Bing Zhang · Dongmei Chen · Lianru Gao
    [Show abstract] [Hide abstract]
    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.
    International Journal of Remote Sensing 07/2015; 36(13):3331-3344. DOI:10.1080/01431161.2015.1042594 · 1.65 Impact Factor
  • Lianru Gao · Bin Yang · Qian Du · Bing Zhang
    [Show abstract] [Hide abstract]
    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.
    Remote Sensing 06/2015; 7(6):6611-6634. DOI:10.3390/rs70606611 · 3.18 Impact Factor
  • Lina Zhuang · Bing Zhang · Lianru Gao · Jun Li · Antonio Plaza
    [Show abstract] [Hide abstract]
    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.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2015; 8(6):2598-2606. DOI:10.1109/JSTARS.2014.2360888 · 3.03 Impact Factor
  • Bin Yang · Minhua Yang · Antonio Plaza · Lianru Gao · Bing Zhang
    [Show abstract] [Hide abstract]
    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.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2015; 8(6):1-12. DOI:10.1109/JSTARS.2015.2388797 · 3.03 Impact Factor
  • Yuanfeng Wu · Jun Li · Lianru Gao · Xuemin Tan · Bing Zhang
    [Show abstract] [Hide abstract]
    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.
    Journal of Applied Remote Sensing 03/2015; 9(1):097295. DOI:10.1117/1.JRS.9.097295 · 1.18 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    IEEE Geoscience and Remote Sensing Letters 02/2015; 12(2):349-353. DOI:10.1109/LGRS.2014.2341044 · 2.10 Impact Factor
  • Bing Zhang · Lina Zhuang · Lianru Gao · Wenfei Luo · Qiong Ran · Qian Du
    [Show abstract] [Hide abstract]
    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.
    IEEE Transactions on Geoscience and Remote Sensing 12/2014; 52(12):7782-7792. DOI:10.1109/TGRS.2014.2319337 · 3.51 Impact Factor
  • Lianru Gao · Qiandong Guo · Antonio Plaza · Jun Li · Bing Zhang
    Journal of Applied Remote Sensing 11/2014; 8(1):083538. DOI:10.1117/1.JRS.8.083538 · 1.18 Impact Factor
  • Source
    Li Ni · Lianru Gao · Shanshan Li · Jun Li · Bing Zhang
    [Show abstract] [Hide abstract]
    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)
    Journal of Applied Remote Sensing 10/2014; 8(1). DOI:10.1117/1.JRS.8.085089 · 1.18 Impact Factor
  • Jianwei Gao · Qian Du · Lianru Gao · Xu Sun · Bing Zhang
    Journal of Applied Remote Sensing 08/2014; 8(1):085094. DOI:10.1117/1.JRS.8.085094 · 1.18 Impact Factor
  • Yuanfeng Wu · Lianru Gao · Bing Zhang · Haina Zhao · Jun Li
    Journal of Applied Remote Sensing 08/2014; 8(1):084797. DOI:10.1117/1.JRS.8.084797 · 1.18 Impact Factor
  • Bing Zhang · Yao Liu · Wenjuan Zhang · Lianru Gao · Jun Li · Jun Wang · Xia Li
    [Show abstract] [Hide abstract]
    ABSTRACT: Image simulation of remote sensing systems is important for the development of new instruments and validations of data processing algorithms. In image simulation process, surface scene simulation is a fundamental issue and usually has the first priority. For two mid-infrared absorption bands near 2.7 µm and 4.3 µm, although there are a lot of applications in remote sensing field, relevant research on surface scene simulation is very limited. In these two mid-infrared ranges, surface radiance is a combination of reflected and emitted radiance. However, the radiance is generally reduced because of strong absorption by atmosphere. Therefore, analysis of surface reflected radiance is essential for simulation work. In this paper, we use a radiative transfer model MODTRAN to simulate proportions of surface reflected radiance for common ground materials under various observation conditions. The obtained results show that proportions of studied materials are 0.8%–99.8% in the band of 2.63–2.83 µm and 1.1%–94.8% in the band of 4.2–4.5 µm. The proportions of surface reflected radiance in both absorption bands are affected by surface reflectivity. In addition, in the band of 2.7 µm the proportion of surface reflected radiance is sensitive to solar geometry, water vapor content and surface temperature, whereas it is insensitive in the band of 4.3 µm. Based on these results, we conduct that both reflection and emission are important for surface scene simulations in the 2.7 µm and 4.3 µm absorption bands.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2014; 7(6):2639-2646. DOI:10.1109/JSTARS.2013.2272633 · 3.03 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2014; 7(6):2351-2366. DOI:10.1109/JSTARS.2014.2302446 · 3.03 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2014; 8(6):1-1. DOI:10.1109/JSTARS.2014.2371615 · 3.03 Impact Factor
  • Xu Sun · Lina Yang · Bing Zhang · Lianru Gao · Liang Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Pixel clustering is a common hyperspectral image processing technique. Its process is to find the appropriate cluster centers and assign each pixel to a center according to a certain metric. Artificial Bee Colony (ABC) algorithm based pattern clustering is proved to have better performance than traditional clustering methods such as K-means. Therefore, studies on hyperspectral image clustering method based on ABC algorithm are done. The target function and feasible solution space are determined, and the complete process is given. The proposed algorithm and other algorithms are compared and analyzed with the use of two sets of real hyperspectral remote sensing data and ground survey results.
    2013 Sixth International Conference on Advanced Computational Intelligence (ICACI); 10/2013
  • Li Ni · Bing Zhang · Lianru Gao · Shanshan Li · Yuanfeng Wu
    [Show abstract] [Hide abstract]
    ABSTRACT: Optical remotely sensed data, especially hyperspectral data have emerged as the most useful data source for regional crop classification. Hyperspectral data contain fine spectra, however, their spatial coverage are narrow. Multispectral data may not realize unique identification of crop endmembers because of coarse spectral resolution, but they do provide broad spatial coverage. This paper proposed a method of multisensor analysis to fully make use of the virtues from both data and to improve multispectral classification with the multispectral signatures convert from hyperspectral signatures in overlap regions. Full-scene crop mapping using multispectral data was implemented by the multispectral signatures and SVM classification. The accuracy assessment showed the proposed classification method is promising.
    SPIE Defense, Security, and Sensing; 05/2013
  • Source
    Shanshan Li · Bing Zhang · An Li · Xiuping Jia · Lianru Gao · Man Peng
    [Show abstract] [Hide abstract]
    ABSTRACT: This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area. A new similarity measurement integrates NHI and spectral information using an adaptive distance norm for clustering. The performance of the proposed neighborhood-constrained-clustering algorithm was assessed through a synthetic image and a real hyperspectral image and compared with those obtained by advanced spectral-spatial clustering algorithms. Experimental results show that the proposed scheme gives better performances.
    IEEE Geoscience and Remote Sensing Letters 05/2013; 10(3):588-592. DOI:10.1109/LGRS.2012.2215005 · 2.10 Impact Factor
  • Bing Zhang · Jianwei Gao · Lianru Gao · Xu Sun
    [Show abstract] [Hide abstract]
    ABSTRACT: Endmember extraction is a vital step in spectral unmixing of hyperspectral images. The Ant Colony Optimization (ACO) algorithm has been recently developed for endmember extraction from hyperspectral data. However, this algorithm may result in a local optimal solution for some hyperspectral images without prescient information, and also has limitation in computational performance. Therefore, in this paper, we proposed several new methods to improve the ACO algorithm for endmember extraction (ACOEE). Firstly, the heuristic information was optimized to improve the algorithm accuracy. In the improved ACOEE, only the pheromones were adopted as the heuristic information when there was no prescient information about hyperspectral data. Then, to enhance algorithm performance, an elitist strategy was proposed to lessen the iteration numbers without reducing the accuracy, and the parallel implementation of ACOEE on graphics processing units (GPUs) also was utilized to shorten the computational time per iteration. The experiment for real hyperspectral data demonstrated that both the endmember extraction accuracy and the computational performance of ACOEE benefited from these methods.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2013; 6(2):522-530. DOI:10.1109/JSTARS.2012.2236821 · 3.03 Impact Factor
  • Lianru Gao · Qian Du · Bing Zhang · Wei Yang · Yuanfeng Wu
    [Show abstract] [Hide abstract]
    ABSTRACT: In the traditional signal model, signal is assumed to be deterministic, and noise is assumed to be random, additive and uncorrelated to the signal component. A hyperspectral image has high spatial and spectral correlation, and a pixel can be well predicted using its spatial and/or spectral neighbors; any prediction error can be considered from noise. Using this concept, several algorithms have been developed for noise estimation for hyperspectral images. However, these algorithms have not been rigorously analyzed with a unified scheme. In this paper, we conduct a comparative study for such linear regression-based algorithms using simulated images with different signal-to-noise ratio (SNR) and real images with different land cover types. Based on experimental results, instructive guidance is concluded for their practical applications.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2013; 6(2):488-498. DOI:10.1109/JSTARS.2012.2227245 · 3.03 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In classifying very high spatial resolution (VHR) hyperspectral imagery, intra-class variation often adversely affects classification accuracy, mainly due to a low signal-to-noise ratio (SNR) and high spatial heterogeneity. To address this problem, this article develops a neighbourhood-constrained k-means (NC-k-means) algorithm by incorporating the pure neighbourhood index into the traditional k-means algorithm. The performance of the NC-k-means algorithm was assessed through a series of simulated images and a real hyperspectral image. The results indicate that the classification accuracy of NC-k-means algorithm is consistently better than that of the traditional k-means algorithm, in particular for the images with significant spatial autocorrelations among neighbouring pixels.
    Remote Sensing Letters 02/2013; 4(2). DOI:10.1080/2150704X.2012.713139 · 1.57 Impact Factor

Publication Stats

125 Citations
55.91 Total Impact Points


  • 2007–2015
    • 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