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LULC classification by semantic segmentation of satellite images using FastFCN

2020-11-13 09:33:03
Md. Saif Hassan Onim, Aiman Rafeed Ehtesham, Amreen Anbar, A. K. M. Nazrul Islam, A. K. M. Mahbubur Rahman

Abstract

This paper analyses how well a Fast Fully Convolu-tional Network (FastFCN) semantically segments satellite images and thus classifies Land Use/Land Cover(LULC) classes. Fast-FCN was used on Gaofen-2 Image Dataset (GID-2) to segment them in five different classes: BuiltUp, Meadow, Farmland, Water and Forest. The results showed better accuracy (0.93), precision (0.99), recall (0.98) and mean Intersection over Union (mIoU)(0.97) than other approaches like using FCN-8 or eCognition, a readily available software. We presented a comparison between the results. We propose FastFCN to be both faster and more accurate automated method than other existing methods for LULC classification.

Abstract (translated)

URL

https://arxiv.org/abs/2011.06825

PDF

https://arxiv.org/pdf/2011.06825.pdf


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