A method of extracting construction areas using Gaofen-1 remote sensing images

Bin Wu; Chao Wang; Qiang Cong; Huan Yin; Jun Zhu; Suju Li; Dongxu He; Anzhi Yue

doi:10.1117/12.2325040

9 October 2018 A method of extracting construction areas using Gaofen-1 remote sensing images

Bin Wu, Chao Wang, Qiang Cong, Huan Yin, Jun Zhu, Suju Li, Dongxu He, Anzhi Yue

Proceedings Volume 10789, Image and Signal Processing for Remote Sensing XXIV; 107891D (2018) https://doi.org/10.1117/12.2325040
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

The precisely extraction of construction areas in remote sensing images can play an important role in territorial planning, land use management, urban environments and disaster reduction. In this article, we propose a method for extracting construction areas using Gaofen-1 panchromatic remote sensing images by adopting the improved Pantex^[1] (a procedure for the calculation of texture-derived built-up presence index) and unsupervised classification. First of all, texture cooccurrence measures of 10 different directions and displacements are calculated. In this step, we improve the built-up presence index that we use the windows size of 21*21 to calculate the GLCM contrast measure instead of 9*9 according to the spatial resolution of Gaofen-1 panchromatic image. Then we use the intersection operator “MIN” to combine the 10 different anisotropic GLCM contrast measure to generate the final built-up presence index result. At last, we use the unsupervised classification method to classify the Pantex result into two classes and the one with larger cluster center is the construction area class. Confusion matrix of Beijing-Tianjin-Hebei region experiment shows that this method can effectively and accurately extract the construction areas in Gaofen-1 panchromatic images with the overall accuracy of more than 92%.

Citation Download Citation

Bin Wu, Chao Wang, Qiang Cong, Huan Yin, Jun Zhu, Suju Li, Dongxu He, and Anzhi Yue "A method of extracting construction areas using Gaofen-1 remote sensing images", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107891D (9 October 2018); https://doi.org/10.1117/12.2325040

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