基于Gabor滤波器的航空图像居民区域提取

谷多玉; 郭江; 李书晓; 常红星

基于Gabor滤波器的航空图像居民区域提取

中国科学院自动化研究所, 北京 100190

基金项目: 国家自然科学基金资助项目(61005028)

详细信息

作者简介:
谷多玉(1984-),女,山东济南人,博士生,duoyu.gu@ia.ac.cn.

中图分类号: TP 391
计量
- 文章访问数: 3399
- HTML全文浏览量: 120
- PDF下载量: 1012
- 被引次数: 0
出版历程
- 收稿日期: 2010-09-06
- 网络出版日期: 2012-01-30

Resident region extraction using Gabor filter in aerial imagery

Institute of Automation, Chinese Academy of Science, Beijing 100190, China

摘要

摘要: 中低分辨率航空图像中居民区域的自动提取对地理信息系统更新和无人机导航具有重要作用.详细分析了Gabor滤波器参数对纹理提取的影响,提出了一种基于Gabor滤波器的居民区快速提取算法.算法包含4步:运用Gabor滤波器分析图像纹理,采用核密度估计生成居民区域置信图像,进而计算自适应阈值分割置信图得到候选区域,最后根据区域几何形状去除干扰得到居民区.算法平均运算时间为0.42s,实验结果表明了算法的高效性和准确性.
- 航空图像 /
- 居民区域提取 /
- Gabor滤波器 /
- 核密度估计
Abstract: Automatic extraction of resident regions from medium and low revolution aerial images plays a crucial role in geographic information system(GIS) updating and unmanned aerial vehicle(UAV) navigation. The parameters of Gabor filter in detail were analyzed, and a fast resident region extraction algorithm based on Gabor filter was proposed. The method composed of four steps. Firstly, the texture features of input image were extracted using Gabor filter. And then the texture features were smoothed by the kernel density estimation method to get the confidence image. Subsequently, the confidence image was segmented by the basic global threshold algorithm to acquire candidate regions. Ultimately, the candidate regions were verified by the geometric structure information. The method has low computational complexity, and can run within 0.42s in average. The efficiency and accuracy of the proposed algorithm were demonstrated by the experimental results.
- aerial imagery /
- resident region extraction /
- Gabor filter /
- kernel density estimation

HTML全文

参考文献(1)

[1] Jolly M P D,Gupta A.Color and texture fusion:application to aerial image segmentation and GIS updating[J].Image and Vision Computing,2000,18(10):823-832 [2] Unsalan C,Boyer K L.Classifying land development in high-resolution panchromatic satellite images using straight-line statistics[J].Geoscience and Remote Sensing,IEEE Transactions on,2004,42(4):907-919 [3] Lorette A,Descombes X,Zerubia J.Texture analysis through a Markovian modelling and fuzzy classification:application to urban area extraction from satellite images[J].International Journal of Computer Vision,2000,36(3):221-236 [4] 文贡坚,李德仁,叶芬.从卫星遥感全色图像中自动提取城市目标[J].武汉大学学报:信息科学版,2003,28(2):212-218Wen Gongjian,Li Deren,Ye Fen.Automatic extraction of urban area from satellite panchromatic remote sensing images[J].Journal of Wuhan University:Geomatics and Information Science,2003,28(2):212-218(in Chinese) [5] Zhong Ping,Wang Runsheng.Using combination of statistical models and multilevel structural information for detecting urban areas from a single gray-level image[J].Geoscience and Remote Sensing,IEEE Transactions on,2007,45(5):1469-1482 [6] Sirmacek B,Unsalan C.Urban area detection using local feature points and spatial voting[J].Geoscience and Remote Sensing Letters,IEEE,2010,7(1):146-150 [7] May S,Inglada J.Urban area detection and segmentation using OTB[C]//Geoscience and Remote Sensing Symposium,2009 IEEE International.Cape Town,South Africa:[s.n.],2009,4:928-931 [8] Wang Min.Influence of number of features on texture based residential area extraction from remotely sensed imagery[C]//Image and Signal Processing,2nd International Congress on.Tianjin:[s.n.],2009:1-4 [9] Pesaresi M,Gerhardinger A,Kayitakire F.A robust built-up area presence index by anisotropic rotation-invariant textural measure[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(3):180-192 [10] Movellan J. Tutorial on gabor filters[R/OL].(1996)http://mplab.ucsd.edu/wordpress/tutorials/gabor.pdf [11] Viola P,Jones M J.Robust real-time face detection [J].Int J Comput Vis,2004,57(2):137-154 [12] Gonzalez Raphael,Woods Richard E.Digital image processing,second edition [M].Beijing:Publishing House of Electronics Industry,2008:598-600 [13] Lipton A J,Fujiyoshi H,Patil R S.Moving target classification and tracking from real-time video[C]//Applications of Computer Vision,Fourth IEEE Workshop on.Princeton,New Jersey:[s.n.],1998:8-14

施引文献

资源附件(0)

访问统计