Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.

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 (B_1–4) 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 B_1–4, 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 B_1–4, 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.