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Li D.,Hubei Engineering University | Zhang G.,CAS Academy of Opto Electronics | Wu Z.,Hubei Engineering University | Yi L.,Hubei Engineering University
IEEE Transactions on Image Processing

This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation. © 2010 IEEE. Source

Li D.,CAS Academy of Opto Electronics
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics

The V-estimator (VE) for K-distribution shape parameter proposed by Oliver in 1993, bears the characteristics of without solving non-linear equations so it has a high estimating efficiency, but the estimating accuracy of it is lower than that of many other moment estimators, sometimes the VE even results in odd value. In order to make the best use of the advantages and bypass the disadvantages, on the basis of derivation and analysis of the VE bias, by means of a set of Monte-Carlo experiments, the V-estimator with corrective term (VCE) was discussed, which overcomes the shortcomings above of the VE. Simulation results show that not only the estimating accuracy of the VCE is significantly superior to the VE, but also, on both of estimating accuracy and efficiency, to the U-estimator considered as the moment estimator with the highest accuracy usually. Especially, experiment results demonstrate that the VCE is better suited to performing in the case of small samples, this feature makes it possible that the VCE is more applicable to the practice. ©, 2015, Beijing University of Aeronautics and Astronautics (BUAA). All right reserved. Source

Zhang K.,CAS Academy of Opto Electronics | Zhang K.,University of Chinese Academy of Sciences | Li X.,Chinese Academy of Sciences | Zhang J.,Chinese Academy of Sciences
IEEE Geoscience and Remote Sensing Letters

Feature point matching is a critical step in feature-based image registration. In this letter, a highly robust feature-point-matching algorithm is proposed, which is based on the feature point descriptor calculated by the triangle-area representation (TAR) of the K nearest neighbors (KNN-TAR). The affine invariant descriptor KNN-TAR is used to find the candidate outliers, and then, the real outliers will be removed by the local structure and global information. The experimental results show that the proposed method can remove the outliers from the initial matching result even when the outliers are of high proportion. Compared with graph transformation matching and restricted spatial-order constraints, KNN-TAR outperforms these methods with higher stability and precision. © 2013 IEEE. Source

Huang Z.,Jiangsu University | Yuan H.,CAS Academy of Opto Electronics
Radio Science

In this article a radial basis function (RBF) neural network improved by Gaussian mixture model is developed to be used for forecasting ionospheric 30 min total electron content (TEC) data given the merits of its nonlinear modeling capacity. In order to understand more about the response of developed network model with respect to stations situated at different latitude, estimated TEC overhead of GPS ground stations BJFS (39.61°N, 115.89°E), WUHN (30.53°N, 114.36°E), and KUNM (25.03°N, 102.80°E) for 6 months in 2011 are used for training data set, validating data and test data set of RBF network model. The performance of the trained model is evaluated at a set of criteria. Our results show that the predicted TEC is in good agreement with observations with mean relative error of about 9% and root-mean-square error of less than 5 total electron content unit, 1 TECU = 1016 el m -2. Our comparison further indicates that RBF network offers a powerful and reliable tool for the design of ionospheric TEC forecast. ©2014. American Geophysical Union. All Rights Reserved. Source

Li D.,CAS Academy of Opto Electronics
Chinese Journal of Aeronautics

Multi-angle synthetic aperture radar (SAR) image matching is very challenging, because the same object may cause different backscattering patterns, heavily depending on the radar incident angle. A technique based on the relations between the invariant positions of ground targets among the reference and sensed images is proposed to accommodate the nonmatching patterns. It involves a target extraction using wavelet coefficient fusion, as well as a geometric voting matching routine for searching the matched control points (CPs) in the reference and sensed images, respectively. To accelerate the speed of the search, a robust, rapidly corresponding CPs determination strategy is exploited by utilizing the global spatial transformation model, as well as a procedure of outlier removal to ensure the desired accuracy. Meanwhile, the positions of the matched point pairs are relocated using mutual information. The final warping of the images according to the CPs is performed by using a polynomial function. The results show the possibility of matching multi-angle SAR images in general cases. © 2015 Production and hosting by Elsevier Ltd. Source

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