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

WEN Qi1 WANG Wei1 LI Ling-ling1 MEI Li-qin2 TAN Yi-hua2

(1.National Disaster Reduction Center of China, Beijing, China 100124)
(2.College of Automation, Huazhong University of Science and Technology, Wuhan, China 430074)

【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-kernel 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 were 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 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.

【Keywords】 high resolution remote sensing image; extraction of built-up area in plain; multi-hypothesis voting; multi-kernel learning; multi-scale segmentation;


【Funds】 National Natural Science Foundation of China (41301485) National Science and Technology Major Project of the Ministry of Science and Technology of China National High-tech R & D Program of China (863 Program)(2013AA122104)

Download this article

(Translated by xiaojin)


    [1]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): 1 156-1 167.

    [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]. IEEEGeoscience and Remote Sensing Letter, 2013, 10(6): 1 300-1 304.

    [4]WANG W, TANG Y P, REN J L, et al. . An improved algorithm for Harris corner detection[J]. Opt. Precision Eng. , 2008, 16(10): 1 995-2 001. (in Chinese)

    [5]WANG F P, SHUI P L. Corner detection via consistency of local directional differential vectors[J]. Opt. Precision Eng. , 2015, 23(12): 3 509-3 518. (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): 1 925-1 939.

    [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]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): 2 722-2 727. (in Chinese)

    [12]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]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]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 scaleinvariant features[C]. IEEE International Conference on Computer Vision, 1999: 1 150-1 157.

    [17]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): 2 274-2 282.

    [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, KAYITA-KIRE 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]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)

This Article


CN: 22-1198/TH

Vol 24, No. 10, Pages 2557-2564

October 2016


Article Outline


  • 1 Introduction
  • 2 Detection of built-up areas in a plain from high resolution remote sensing images
  • 3 Experimental results and analysis
  • 4 Conclusion
  • References