Generating a highly detailed DSM from a single high-resolution satellite image and an SRTM elevation model

In this paper, a different approach based on convolutional neural networks (CNNs) is proposed to generate digital surface model (DSM) from a single high-resolution satellite image. In this regard, an approach based on a deep convolutional neural network was designed. The proposed CNN has an encoder-decoder structure to extract multi-scale features in the encoding part and estimate the height values by up-sampling the extracted abstract features. Then, a filtering approach based on morphological operators is proposed to extract the non-ground pixels from each estimated height image. The final digital surface mode Shuttle Radar Topography Mission (SRTM) is obtained by integrating the SRTM elevation model and extracted non-ground objects. Evaluating the estimated height images indicated 0.219, 0.865, and 2.912 m on average log10 error, relative error, and root mean square error (RMSE), respectively. In addition, investigating the final integrated DSM indicated 4.625 m on average for RMSE, demonstrating a promising performance of the proposed approach.

Automated summary

This paper proposes a method to generate DSM using CNN, which successfully extracts features and creates a digital surface model through deep CNN structure and filters. The final integrated DSM shows high accuracy.