A new automated remote sensing segmentation pipeline, EcoMapper, plus an empirical index relating achievable segmentation quality to feature size and image resolution.
LULC classification by semantic segmentation of satellite images using FastFCN
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abstract
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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Segmentation of arbitrary features in very high resolution remote sensing imagery
A new automated remote sensing segmentation pipeline, EcoMapper, plus an empirical index relating achievable segmentation quality to feature size and image resolution.