LULC classification by semantic segmentation of satellite images using FastFCN

TL;DR

This paper evaluates FastFCN for semantic segmentation of satellite images, demonstrating high accuracy and efficiency in classifying land use and land cover types compared to other methods.

Contribution

It introduces FastFCN as a faster and more accurate approach for LULC classification in satellite imagery, outperforming existing methods.

Findings

Achieved 0.93 accuracy and 0.97 mIoU in LULC classification

FastFCN outperforms FCN-8 and eCognition in accuracy and speed

Demonstrated effectiveness on Gaofen-2 dataset

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.