Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning

Abstract

Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately capturing small abandoned cropland, particularly in regions, such as southern China, with fragmentized farmland, poses a significant challenge using the traditional optical image-based mapping methods due to their coarse spatial resolution. In addition, irregular and chaotic textures of abandoned cropland further complicate the accurate prediction using very high resolution (VHR) data. In this article, we propose a novel deep learning network termed pixel attention contrastive network (PACnet) to map fine-grained abandoned cropland based on VHR data. Cross-image pixel contrast learning is introduced to discern distinctive features distinguishing abandoned cropland from other land types across various interimages. Moreover, a criss-cross attention module is embedded to enhance the contrasting characteristics within individual intraimages. Experimental outcomes validate the efficacy of PACnet, showcasing the highest accuracy (OA = 93.8% and mIOU = 71.7%) when compared with classical semantic segmentation networks. Our proposal not only underscores the potency of VHR remote sensing data in finely delineating abandoned cropland but also carries significant implications for cropland abandonment impact analysis and informed policy formulation.

Keywords

• Abandoned cropland
• contrastive learning
• deep learning (DL)
• very high resolution (VHR)