Generating a highly detailed DSM from a single high-resolution satellite image and an SRTM elevation model. Issue 4 (3rd April 2021)

• Record Type: Journal Article
• Authors: Amini Amirkolaee, Hamed; Arefi, Hossein
• Is Part Of: Remote sensing letters. Volume 12:Issue 4(2021)
• Page Start: 335
• Page End: 344
• Publication Date: 2021-04-03
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2021.1880659
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: 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.