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高分辨率遥感影像的平原建成区提取

Extraction of built-up area in plain from high resolution remote sensing images

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摘要

通过分析高分辨率遥感影像中平原建成区的纹理特征和局部关键点特征,提出了基于多核学习、多尺度分割以及多假设投票的平原建成区提取方法。该方法利用MR8纹理特征和尺度不变特征变换(SIFT)算法提取建成区,融合多个特征进行学习和分类,从而加强了分类器的鲁棒性和稳定性,提高了检测准确率。该方法还通过超像素分割和多假设投票将基于图像块的判别结果转化为基于像素的检测结果,完全消除块状效应,使得目标区域具有准确的边缘和形状。在多幅GF-1卫星遥感图像上进行测试,结果显示: 提出方法的平均检测精度为80%,平均召回率高于85%,平均F值可达80%以上,综合指标高于其他方法,验证了提取平原地形建成区的可行性和准确性。由于建成区提取结果已精确到了像素级别,同时避免了漏检和误检,提取出的建成区影像很准确。

Abstract

By analyzing the textural features and local key points of the built-up area in a plain from high resolution remote sensing images, a method to extract the built-up area in the plain was proposed based on multi-core learning, multi-scale segmentation and multi-hypothesis voting. With the proposed method, MR8 texture characteristics and Scale Invariant Feature Transform (SIFT) algorithmwere used to extract the built-up area, and multi-characteristics was fused to implement the learning and classification to improve the robustness and stability of classifiers and to enhance the detection accuracy. Then, based on the pixel segmentation and multi-hypothesis voting, the discriminant result based on image blocks was translated into test result based on pixels to completely eliminate the block effect and to make the target area showing precise edges and shapes. The proposed method has been validated in GF-1 satellite images. The results show that the average detection precision, average recall rate and the average F-measure of the method have been achieved above 80%, 85% , and 80%, respectively. Moreover, its comprehensive performance is better than that of other methods. These results demonstrate the feasibility and accuracy of this method. As extraction precision of the built-up area has been to be the pixel level and the leak detection and error detection have been avoided, the built up area images extracted are very accurate.

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补充资料

中图分类号:TP751;TP79

DOI:10.3788/ope.20162410.2557

所属栏目:信息科学

基金项目:国家自然科学基金资助项目(No.41301485); 国家科技重大专项资助项目; 国家863计划资助项目(No.2013AA122104)

收稿日期:2016-06-13

修改稿日期:2016-08-14

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作者单位    点击查看

温 奇:民政部国家减灾中心,北京 100124
王 薇:民政部国家减灾中心,北京 100124
李苓苓:民政部国家减灾中心,北京 100124
梅立琴:华中科技大学 自动化学院, 湖北 武汉 430074
谭毅华:华中科技大学 自动化学院, 湖北 武汉 430074

联系人作者:温奇(whistlewen@aliyun.com)

备注:温 奇(1983-),男,山西洪洞人,博士,副研究员,2004年于北京理工大学获得学士学位,2009年于中国科学院遥感应用研究所获得博士学位,主要从事高分辨率遥感减灾应用方面的研究。

【1】史伟国,周立民,靳颖. 全球高分辨率商业遥感卫星的现状与发展[J]. 卫星应用, 2012(3): 43-50.
SHI W G, ZHOU L M, JIN Y. The present situation and development of the global commercial high resolution remote sensing satellite [J]. Satellite Application, 2012(3): 43-50.(in Chinese)

【2】SIRMACEK B, UNSALAN C. Urban-area and building detection using SIFT keypoints and graph theory [J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(4): 1156-1167.

【3】TAO C, TAN Y H, ZOU Z R, et al.. Unsupervised detection of built-up areas from multiple high-resolution remote sensing images [J]. IEEE Geoscience and Remote Sensing Letter, 2013, 10(6): 1300-1304.

【4】王崴, 唐一平, 任娟莉, 等. 一种改进的Harris角点提取算法[J]. 光学 精密工程, 2008, 16(10): 1995-2001.
WANG W, TANG Y P, REN J L, et al.. An improved algorithm for Harris corner detection[J]. Opt. Precision Eng., 2008, 16(10): 1995-2001. (in Chinese)

【5】王富平, 水鹏朗. 利用局部方向微分向量一致性的角点检测[J]. 光学 精密工程, 2015, 23(12): 3509-3518.
WANG F P, SHUI P L. Corner detection via consistency of local directional differential vectors[J]. Opt. Precision Eng., 2015, 23(12): 3509-3518. (in Chinese)

【6】HUANG X, ZHANG L P. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1): 161-172.

【7】BARNSLEY M J, RARR S L. Inferring urban land use from satellite sensor images using kernel-based spatial reclassification [J]. Photogrammetric Engineering and Remote Sensing, 1996, 62(7): 949-958.

【8】YU S, BERTHOD M, GIRAUDON G. Toward robust analysis of satellite images using map information-application to urban area detection [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(4): 1925-1939.

【9】WEIZMAN L, GOLDBERGER J. Detection of urban zones in satellite images using visual words [C]. IEEE Conference on Geoscience and Remote Sensing Symposium, 2008: 160-163.

【10】TAO C, TAN J G, YU Y H, et al.. Urban area detection using multiple kernel learning and graph cut [C]. IEEE Conference on Geoscience and Remote Sensing Symposium,2012:83-86.

【11】何林阳, 刘晶红, 李刚, 等. 改进BRISK特征的快速图像配准算法[J]. 红外与激光工程, 2014, 43(8): 2722-2727.
HE L Y, LIU J H, LI G, et al.. Fast image registration approach based on improved BRISK [J]. Infrared and Laser Engineering, 2014, 43(8): 2722-2727. (in Chinese)

【12】王志强, 程红, 李成, 等. 全局图像配准的目标快速定位方法[J]. 红外与激光工程, 2015, 44(s): 225-229.
WANG ZH Q, CHENG H, LI CH, et al.. Fast target location method of global image registration [J]. Infrared and Laser Engineering, 2015, 44(s): 225-229. (in Chinese)

【13】丘文涛, 赵建, 刘杰. 结合区域分割的图像SIFT匹配方法[J]. 液晶与显示, 2012, 27(6): 827-831.
QIU W T, ZHAO J, LIU J. Image matching algorithm combining SIFT with region segmentation[J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(6): 827-831. (in Chinese)

【14】王灿进, 孙涛, 陈娟. 局部不变特征匹配的并行加速技术研究[J]. 液晶与显示, 2014, 29(2): 266-274.
WANG C J, SUN T, CHEN J. Speeding up local invariant feature matching using parallel technology [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(2): 266-274. (in Chinese)

【15】VAPNIKVN.The Nature of Statistical Learning Theory [M]. New York : Springer-Berlag, 1995.

【16】LOWE G D. Object recognition from local scale-invariant features [C]. IEEE International Conference on Computer Vision, 1999:1150-1157.

【17】王睿, 朱正丹. 融合全局-颜色信息的尺度不变特征变换[J]. 光学 精密工程,2015, 23(1): 295-301.
WANG R, ZHU ZH D. SIFT matching with color invariant characteristics and global context[J]. Opt. Precision Eng., 2015, 23(1):295-301. (in Chinese)

【18】ACHANTA R, SHAJI A, SMITH K, et al.. SLIC superpixels compared to state-of-the-art superpixel methods [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.

【19】ROUSE J W, HAAS R H, SCHELL J A, et al.. Monitoring the vernal advancement of natural vegetation, Final report[R]. NASA/GCSFC, Greenbelt, MD, 1974.

【20】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.

【21】KOVACS A, SZIRANYI T. Improved harris feature point set for orientation-sensitive urban-area detection in aerial images [J]. IEEE Geoscience and Remote Sensing Letter, 2013, 10(4): 796-800.

【22】黄昕. 高分辨率遥感影像多尺度纹理、形状、特征提取与面向对象分类方法研究[D]. 武汉: 武汉大学, 2009.
HUANG X. Multi-scale texture and shape feature extraction and object-oriented classification for very high resolution remotely sensed imagery [D]. Wuhan: Wuhan University,2009. (in Chinese)

引用该论文

WEN Qi,WANG Wei,LI Ling-ling,MEI Li-qin,TAN Yi-hua. Extraction of built-up area in plain from high resolution remote sensing images[J]. Optics and Precision Engineering, 2016, 24(10): 2557-2564

温 奇,王 薇,李苓苓,梅立琴,谭毅华. 高分辨率遥感影像的平原建成区提取[J]. 光学 精密工程, 2016, 24(10): 2557-2564

被引情况

【1】王 宇,王宝山,王 田,杨 艺. 面向遥感图像水域分割的图像熵主动轮廓模型. 光学 精密工程, 2018, 26(3): 698-707

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