Comparing CNN Architectures for Land Cover Classification on Multispectral Images

Abstract

We investigate the benefits of using multispectral images for land cover classification. To perform this comparative analysis, we present a novel model, LandNet which is configurable with multiple deep residual networks to extract features on several combinations of bands. We consider both the classification accuracy of the various LandNet configurations. We perform this study on the EuroSAT and BigEarthNet datasets, both of which contain multispectral images from the Sentinel-2 mission. On EuroSAT, we convincingly demonstrate marked improvements in the accuracy of around 1% to 97.815% when using additional bands compared to merely using the RGB bands. On BigEarthNet we show the additional bands are able to improve the recall of the LandNet by 0.04. We achieve a precision score of 0.85, recall of 0.80 and an F-score of 0.82. The precision, recall and F-score we achieve outperform prior results achieved on BigEarthNet.