Multispectral Semantic Land Cover Segmentation From Aerial Imagery With Deep Encoder–Decoder Network

Abstract

Developing accurate algorithms for agricultural pattern recognition from aerial imagery has become increasingly important due to the prevalence of unmanned aerial vehicles (UAVs). This letter introduces a deep encoder–decoder network for semantic land cover segmentation, where the goal is to classify six anomaly categories from multispectral aerial imagery. Since aerial imagery exhibits specific characteristics and visual challenges in this imaging domain, existing semantic segmentation models are not plug-and-play. Starting from a state-of-the-art segmentation model, we present a step-by-step analysis of key challenges and also reveal our observations in addressing these challenges. In particular, we investigate on how to exploit data prior knowledge, how to deal with sample imbalance, and how to encode global semantic and contextual information to improve segmentation. Experiments on a recent large-scale aerial land cover data set demonstrate that our method achieves compelling performance against other state-of-the-art approaches. Our results and insights can provide references for practitioners working in this field when dealing with similar segmentation problems.