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LULC classification by semantic segmentation of satellite images using FastFCN

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arxiv 2011.06825 v2 pith:DXRPZ7W4 submitted 2020-11-13 cs.CV cs.LG

Md. Saif Hassan Onim , Aiman Rafeed Ehtesham , Amreen Anbar , A. K. M. Nazrul Islam , A. K. M. Mahbubur Rahman This is my paper · ORCID
classification cs.CVcs.LG
keywords fastfcnlulcclassesclassificationimageslandotherresults
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This paper analyses how well a Fast Fully Convolutional 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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  1. Segmentation of arbitrary features in very high resolution remote sensing imagery

    cs.CV 2024-12 reject novelty 4.0 of 10

    A new automated remote sensing segmentation pipeline, EcoMapper, plus an empirical index relating achievable segmentation quality to feature size and image resolution.

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