LULC classification by semantic segmentation of satellite images using FastFCN
This provides a faster and more accurate automated method for LULC classification in remote sensing, though it is incremental as it builds on existing segmentation techniques.
The paper tackled land use/land cover classification by applying FastFCN to segment satellite images, achieving high accuracy (0.93), precision (0.99), recall (0.98), and mIoU (0.97) on the GID-2 dataset.
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.