6+: A Novel Approach for Building Extraction from a Medium Resolution Multi-Spectral Satellite

Mayank Dixit, Kuldeep Chaurasia, Vipul Kumar Mishra, Dilbag Singh and Heung-No Lee
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Mayank Dixit: School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
Kuldeep Chaurasia: School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
Vipul Kumar Mishra: School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
Dilbag Singh: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
Heung-No Lee: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea

Sustainability, 2022, vol. 14, issue 3, 1-15

Abstract: For smart, sustainable cities and urban planning, building extraction through satellite images becomes a crucial activity. It is challenging in the medium spatial resolution. This work proposes a novel methodology named ‘6+’ for improving building extraction in 10 m medium spatial resolution multispectral satellite images. Data resources used are Sentinel-2A satellite images and OpenStreetMap (OSM). The proposed methodology merges the available high-resolution bands, super-resolved Short-Wave InfraRed (SWIR) bands, and an Enhanced Normalized Difference Impervious Surface Index (ENDISI) built-up index-based image to produce enhanced multispectral satellite images that contain additional information on impervious surfaces for improving building extraction results. The proposed methodology produces a novel building extraction dataset named ‘6+’. Another dataset named ‘6 band’ is also prepared for comparison by merging super-resolved bands 11 and 12 along with all the highest spatial resolution bands. The building ground truths are prepared using OSM shapefiles. The models specific for extracting buildings, i.e., BRRNet, JointNet, SegUnet, Dilated-ResUnet, and other Unet based encoder-decoder models with a backbone of various state-of-art image segmentation algorithms, are applied on both datasets. The comparative analyses of all models applied to the ‘6+’ dataset achieve a better performance in terms of F1-Score and Intersection over Union (IoU) than the ‘6 band’ dataset.

Keywords: deep learning; building extraction; built-up index; super-resolution; multispectral; satellite images (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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