Lianru Gao

Northeast Institute of Geography and Agroecology, Peping, Beijing, China

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Publications (29)20.35 Total impact

  • Bing Zhang, Jianwei Gao, Lianru Gao, Xu Sun
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    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 01/2013; 6(2):522-530. · 2.87 Impact Factor
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    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 01/2013; 10(3):588-592. · 1.82 Impact Factor
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    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 01/2013; 6(2):488-498. · 2.87 Impact Factor
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    ABSTRACT: Target detection is an important research content in hyperspectral remote sensing technology, which is widely used in securities and defenses. Nowadays, many target detection algorithm have been proposed. One of the key evaluation indicators of these algorithms performance is false-alarm rate. The feature-level fusion of different target detection results is a simple and effective method to reduce false-alarm rate. But the different value ranges of different algorithms bring difficulties for data fusion. This paper proposed a feature-level fusion method based on RXD detector, which is to integrate multiple target detection results into a multi-bands image, and fuse detection results using principal theory of abnormal detection. Experiments revealed that, this method is not restricted by the quantity of target detection algorithms and not influenced by different value ranges of different algorithms, which can reduce false-alarm rate effectively.
    Proc SPIE 05/2012;
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    ABSTRACT: An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.
    IEEE Geoscience and Remote Sensing Letters 10/2011; · 1.82 Impact Factor
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    ABSTRACT: Markov random field (MRF) provides a useful model for integrating contextual information into remote sensing image classification. However, there are two limitations when using the conventional MRF model in hyperspectral image classification. First, the maximum likelihood classifier used in MRF to estimate the spectral-based probability needs accurate estimation of covariance matrix for each class, which is often hard to obtain with a small number of training samples for hyperspectral imagery. Second, a fixed spatial neighboring impact parameter for all pixels causes overcorrection of spatially high variation areas and makes class boundaries blurred. This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery. An adaptive spatial neighboring impact parameter is assigned to each pixel according to its spatial contextual correlation. Experimental results of a hyperspectral image show that the classification accuracy from the proposed method has been improved compared to those from the conventional MRF model and pixel-wise classifiers including the maximum likelihood classifier and SVM classifier.
    Journal of Applied Remote Sensing 01/2011; 5(1):3538-. · 0.88 Impact Factor
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    ABSTRACT: The study on the influence of noise is never discontinuous in hyperspectral image processing. This paper studies this key element in dimension reduction methods based on orthogonal transformation of hyperspectral images. Firstly, distribution features of noise in spectral and spatial dimension are analyzed. Then several traditional dimension reduction methods are discussed. And, noise estimation methods based on spectral and spatial correlation are applied on Maximum Noise Fraction (MNF) transform respectively. From the experimental analysis, it is found that spectral and spatial de-correlation algorithm with image regular partitioning (e.g. rectangle) is more suitable for noise matrix estimation in MNF. Finally, these dimension reduction methods are contrastively used for extracting information from hyperspectral images. From the comparison of results, the optimized MNF considering characteristics of noise can extract more efficient features than others.
    Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011 3rd Workshop on; 01/2011
  • Bing Zhang, Xun Sun, Lianru Gao, Lina Yang
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    ABSTRACT: Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. On the basis of linear spectral mixture models, this paper applied directed and weighted graphs to describe the relationship between pixels. In particular, we transformed the endmember extraction problem in the decomposition of mixed pixels into an issue of optimization and built feasible solution space to evaluate the practical significance of the objective function, thereby establishing two ant colony optimization algorithms for endmember extraction. In addition to the detailed process of calculation, we also addressed the effects of different operating parameters on algorithm performance. Finally we designed two sets of simulation data experiments and one set of actual data experiments, and the results of those experiments prove that endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, de- crease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N-FINDR and VCA algorithms.
    IEEE Transactions on Geoscience and Remote Sensing 01/2011; 49:2635-2646. · 3.47 Impact Factor
  • Bing Zhang, Xun Sun, Lianru Gao, Lina Yang
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    ABSTRACT: This paper described endmember extraction as a combinatorial optimization problem (COP). By defining parti- cles' position and velocity, discrete particle swarm optimization (D-PSO) was proposed based on particle swarm optimization to resolve COP. The algorithm was tested and evaluated by hyper- spectral remote sensing data. Experimental results showed that, while extracting the same number of endmembers, D-PSO could get a smaller root-mean-square error between an original image and its remixed image on the precondition of correct extraction results compared to the algorithms of vertex component analysis (VCA) and N-FINDR, which meant that D-PSO could acquire higher extraction precision.
    IEEE Transactions on Geoscience and Remote Sensing 01/2011; 49:4173-4176. · 3.47 Impact Factor
  • IEEE Geosci. Remote Sensing Lett. 01/2011; 8:973-977.
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    ABSTRACT: Changes have been made to this article. See the full text for a description of the changes.
    Journal of Applied Remote Sensing 01/2011; 5(1):0101-. · 0.88 Impact Factor
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    ABSTRACT: This paper presents whole pre-processing streamline of Beijing-1 small satellite images and focuses on some of the key issues in specific or improved data acquisition and processing. Characteristics of small satellite and peculiarities in the image pre-processing are analyzed, design and skeleton of the pre-processing system is expounded, and then, some of the key issues encountered and explored during processing of Beijing-1 small satellite data are discussed. The discussed issues include relative calibration, onboard compression, jitter removal and exposure control. The works in this paper are done with integration exploration based on systematic consideration of whole imaging and processing process, and are all testified with practical implementation.
    Proc SPIE 10/2009;
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    ABSTRACT: Performance of the selected algorithm is crucial for hyperspectral target detection. However, it is sure that there is no algorithm that is appropriate for all hyperspectral data or applications. Result of detection can be affected by many factors, including spectral feature of targets and backgrounds, characteristic of spectrometer and pre-processing method. Thus to improve practicability of hyperspectral target detection, studies should not be focused solely on the algorithms, but also be extended to discussion of above affecting factors. In this paper, hyperspectral imagery measurements and data processing are studied. Six aspects including paints, spectral response, imaging spectrometer mode, image noise and dimension reduction are discussed. Typical abnormity and matching algorithm are applied on images simulated with different factors. The influences of these factors on target detection are analyzed. At last, this research provides a strategy for precision control of target detection in hyperspectral imagery. Researches in this paper can be of great help for algorithm development and algorithm selection in diversified hyperspectral data and applications.
    Proc SPIE 10/2009;
  • Shanshan Li, Bing Zhang, Lianru Gao, Xu Sun
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    ABSTRACT: Remote sensing has been widely used for urban planning, urban environment and resources management. Recognition of some small targets is crucial for these purposes, and hyperspectral data can be used to extract targets with its high spectral resolution. This paper presented several representative algorithms of small targets detection in hyperspectral image, and four abnormal detect methods were applied to find vehicles in urban areas. Comparison of presented results is also being conducted to verify that abnormal detecting methods can be used for urban targets suitably and effectively.
    Urban Remote Sensing Event, 2009 Joint; 06/2009
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    ABSTRACT: This paper gives a systematic exploration for exposure adjustment of satellite cameras. First, theories of satellite exposure adjustment are summarized and the affecting factors are discussed. Then, based on defects of current adjustment method adopted on Beijing-1 small satellite, an improved exposure adjustment method that combines sun elevation angle and type of imaged target is proposed, a lookup table that consists of the parameters for exposure setting of different land cover types is provided based on DN statistics and apparent radiance simulation, and the exposure adjustment strategy is also discussed. Serial experiments are conducted for testament of the proposed method, results show that the proposed strategy is applicable and can improve quality of the acquired images.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
  • IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
  • Xu Sun, Bing Zhang, Lianru Gao, Lina Yang
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    ABSTRACT: In this article, based on the theory of function series approaching, we change the spectral dimension of the hyperspectral data by using the Discrete Fourier transformation, and get a new feature space which could show the shape point of the spectrum curve. The coefficient, which hyperspectral data's component in the new feature space has against the Fourier series, could tell us the effect of different spectral function to the shape of spectrum curve. The paper especially analyzes the possible effect of this feature space in image shadow recognition and precision improvement of unsupervised classification based on the Euclid distance, and verify via experiments.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
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    ABSTRACT: Compression has become a must for efficient storing and transmission of data acquired by satellites with increasing resolution and swath. However, for compression of raw data satellite images, impact of striping noise is inevitable. Variances of Digital Number (DN) values introduced by striping noise will surely impair continuity and smoothness of satellite image and reduce the efficiency of onboard compression. In this paper, using Beijing-1 small satellite images, origin and characteristics of striping noise caused by double channel linear CCD and its impacts on the compression process are analyzed. Then, based on properties of striping noise, an improved method for compression of raw data satellite images is proposed. The new compression method is applied to Beijing-1 small satellite raw data images and yields significant boost in compression performance. Ideas of the proposed algorithm can be easily realized with circuit modification and no adaptation is needed for post processing of the compressed images.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
  • IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
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    ABSTRACT: Feature extraction is often performed to reduce spectral dimension of hyperspectral images before image classification. The maximum noise fraction (MNF) transform is one of the most commonly used spectral feature extraction methods. The spectral features in several bands of hyperspectral images are submerged by the noise. The MNF transform is advantageous over the principle component (PC) transform because it takes the noise information in the spatial domain into consideration. However, the experiments described in this paper demonstrate that classification accuracy is greatly influenced by the MNF transform when the ground objects are mixed together. The underlying mechanism of it is revealed and analyzed by mathematical theory. In order to improve the performance of classification after feature extraction when ground objects are mixed in hyperspectral images, a new MNF transform, with an improved method of estimating hyperspectral image noise covariance matrix (NCM), is presented. This improved MNF transform is applied to both the simulated data and real data. The results show that compared with the classical MNF transform, this new method enhanced the ability of feature extraction and increased classification accuracy.
    Science in China Series F Information Sciences 01/2009; 52:1578-1587. · 0.66 Impact Factor