遥感影像主特征线检测
Principal line detection in remote sensing image
- 2017年21卷第2期 页码:228-238
纸质出版日期: 2017-3 ,
录用日期: 2016-09-02
DOI: 10.11834/jrs.20176234
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纸质出版日期: 2017-3 ,
录用日期: 2016-09-02
扫 描 看 全 文
戴激光, 张力, 朱恩泽, 等. 遥感影像主特征线检测[J]. 遥感学报, 2017,21(2):228-238.
Jiguang DAI, Li ZHANG, Enze ZHU, et al. Principal line detection in remote sensing image[J]. Journal of Remote sensing, 2017,21(2):228-238.
受到成像规律性差,背景纹理复杂及强烈噪声的影响,直线检测方法通常难以适应遥感影像处理的需求。有鉴于此,论文提出一种具有视觉显著性的遥感影像主特征线检测方法。论文首先论证了利用已提取直线为基元,基于格式塔法则构建主特征线的可行性;其次对直线在主特征线上的复杂投影情况进行了详细的剖析,并给出了主特征线的定义;接着建立了主特征线累计权重矩阵及直线统计矩阵,依据格式塔原则分析直线权重分布规律,以此构建了直线的权重模型,同时探讨不同直线在同一主特征线上权重分配规律;最后依据上述分析结果提出了具体的算法步骤。通过多幅含有强烈噪声的光学与SAR遥感卫星影像实验结果表明,相对于其他聚类算法,论文算法能够在杂乱无序的直线集中提取较为清晰的主特征线,并且实验效果基本符合人工视觉感知,便于机器对遥感影像的清晰理解。
The linear detection method is usually difficult to adapt to the demand of remote sensing image processing because of it exhibits poor imaging regularity
complex background texture
and strong noises. To address these problems
we proposed a new method that possesses visual saliency in the remote sensing image and can detect principal features. First
the possibility of constructing principal lines according to the Gestalt laws and the use of extracted lines as geometric primitives were analyzed. The complex projection of the lines in the principal lines was then examined
and the definition of the principal lines was provided. Furthermore
the cumulative weight matrix of the principal line and linear statistical matrix were constructed. Meanwhile
the distribution regularity of the short line weight was studied according to the Gestalt laws
and the model of the short linear weight was constructed. Accordingly
the weight allocation pattern of the different lines in the same principal line was also discussed. Finally
detailed algorithm steps were proposed according to these analyses.The key algorithm steps were described as follows: first
the chain code marshaling algorithms were employed to extract the straight lines. Second
the accumulative weighted matrix and linear statistical matrix of the principal lines were constructed. Third
the lines were sorted on the basis of their spatial positions. Fourth
according to linear weight distribution regularity
all the lines were elected to a cumulative weight matrix according to the linear weight model and distribution rules
and the results were recorded in the linear statistical matrix. Fifth
the local maximum value of the accumulative matrix was obtained to prevent parallel overlapping among the principal lines. Sixth
constraint analysis on the continuity and purity of the accumulative weighted matrix and linear statistical matrix was conducted to prevent the appearance of false principal lines. Finally
the parameters of the principal lines were obtained according to the sorting results of the weight voting matrix and weight values. Meanwhile
the principal line was obtained through its endpoints.The results of multiple SAR and optical remote sensing satellite images with strong noises showed that the traditional line extraction method can obtain only the disordered linear information
which is not clear and useful for image processing. In this study
our proposed method obtained clear principal lines by using Gestalt law on the basis of traditional linear extraction algorithm
and the results were basically in agreement with artificial visual perception. Meanwhile
the results suggest that our algorithm is superior to the traditional cluster algorithm in terms of operation efficiency and experimental effects. The experimental results indicate the potential application of our method in various fields
such as road extraction
image matching
and object recognition. However
this method also presents several shortcomings. First
the extraction results of the principal lines rely heavily on previous results. In addition
whether the linear-weighted Gaussian model established in this study is in full compliance with the Gestalt law requires further investigation. Finally
several parameter settings are experience values acquired by a large number of experiments. Thus
we hope to achieve the adaptive processing of these parameters in our future research.
主特征线格式塔法则视觉显著性累计权重矩阵直线统计矩阵
principal linesGestalt lawsvisual saliencycumulative weight matrixlinear statistical matrix
Ballard D H. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2): 111–122
Barni M, Cappellin V, Paoli A and Mecocci A. 1996. Unsupervised detection of straight lines through possibilistic clustering // Proceedings of the International Conference on Image Processing. Lausanne, Switzerland: IEEE: 963–966
Burns J B, Hanson A R and Riseman E M. 1986. Extracting straight lines. IEEE Transaction on Pattern Analysis and Machine Intelligent, PAMI-8(4): 425–455 [DOI: 10.1109/TPAMI.1986.4767808]
陈仁杰, 刘利刚, 董光昌. 2010. 图像主特征直线的检测算法. 中国图象图形学报, 15(3): 403–408
Chen R J, Liu L G and Dong G C. 2010. Detection of principal lines in images. Journal of Image and Graphics, 15(3): 403–408
Chung K L, Chen T C and Yan W M. 2004. New memory- and computation-efficient Hough transform for detecting lines. Pattern Recognition, 37(5): 953–963
Cuneyt A and Cihan T. 2011. EDLines: a real-time line segment detector with a false detection control. Pattern Recognition Letters, 32(13): 1633–1642
Fiete R D and Tantalo T. 2001. Comparison of SNR image quality metrics for remote sensing systems. Optical Engineering, 40(4): 574–585
Freeman H. 1970. Boundary encoding and processing // Picture Processing and Psychopictorics. New York: Academic Press: 241–266
李翠华, 施华, 戴平阳, 陈婧, 杜晓凤, 曲延云, 谢怡. 2011. 引入格式塔理论的超分辨率图像重建技术. 厦门大学学报(自然科学版), 50(2): 261–270
Li C H, Shi H, Dai P Y, Chen J, Du X F, Qu Y Y and Xie Y. 2011. Super-resolution image reconstruction based on gestalt theory. Journal of Xiamen University (Natural Science), 50(2): 261–270
Stahl J S and Wang S. 2008. Globally optimal grouping for symmetric closed boundaries by combining boundary and region information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3): 395–411
Tsuda K, Minoh M and Ikeda K. 1996. Extracting straight lines by sequential fuzzy clustering. Pattern Recognition Letters, 17(6): 643–649
Von Gioi R G, Jakubowicz J, Morel J M and Randall G. 2010. LSD: a fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligent, 32(4): 722–732
王竞雪, 宋伟东, 赵丽科, 王伟玺. 2014a. 改进的Freeman链码在边缘跟踪及直线提取中的应用研究. 信号处理, 30(4): 422–430
Wang J X, Song W D, Zhao L K and Wang W X. 2014a. Application of improved freeman chain code in edge tracking and straight line extraction. Journal of Signal Processing, 30(4): 422–430
王竞雪, 朱庆, 王伟玺, 赵丽科. 2014b. 结合边缘编组的Hough变换直线提取. 遥感学报, 18(2): 378–389
Wang J X, Zhu Q, Wang W X and Zhao L K. 2014b. Straight line extraction algorithm by Hough transform combining edge grouping. Journal of Remote Sensing, 18(2): 378–389
Wu B, Zhang Y S and Zhu Q. 2012. Integrated Point and Edge Matching on Poor Textural Images Constrained by self-adaptive triangulations. ISPRS Journal of Photogrammetry and Remote Sensing, 68: 40–55
徐威, 唐振民, 徐丹, 吴国星. 2015. 融合多特征与格式塔理论的路面裂缝检测. 计算机辅助设计与图形学学报, 27(1): 147–156
Xu W, Tang Z M, Xu D and Wu G X. 2015. Integrating multi-features fusion and gestalt principles for pavement crack detection. Journal of Computer-Aided Design and Computer Graphics, 27(1): 147–156
Xu Z Z, Shin B S and Klette R. 2015. Accurate and robust line segment extraction using minimum entropy with Hough transform. IEEE Transactions on Image Processing, 24(3): 813–822
曾接贤, 王玉. 2015. 结合格式塔完形规则的自然图像分割. 中国图象图形学报, 20(8): 1026–1034
Zeng J X and Wang Y. 2015. Natural image segmentation method based on Gestalt rules. Journal of Image and Graphics, 20(8): 1026–1034
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