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LULC classification by semantic segmentation of satellite images using FastFCN

13 November 2020
Md. Saif Hassan Onim
Aiman Rafeed Ehtesham
Amreen Anbar
A. Islam
A. Rahman
ArXiv (abs)PDFHTML
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.

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