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Remote Sens. 2016, 8(11), 969; doi:10.3390/rs8110969

Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yuhong He, Qihao Weng, Richard Gloaguen and Prasad S. Thenkabail
Received: 25 July 2016 / Revised: 7 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
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Abstract

Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B14) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B14, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B14, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features. View Full-Text
Keywords: SAR; optical; building density; CART; fractal dimension; lacunarity SAR; optical; building density; CART; fractal dimension; lacunarity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, Y.; Lin, C.; Wang, S.; Liu, W.; Tian, Y. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data. Remote Sens. 2016, 8, 969.

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