Work

A Method for the Extraction of Apocynum venetum L. Spatial Distribution in Yuli County, Xinjiang, via an Improved SegFormer Network

Year: 2025

Type: article

Abstract: Efficient and accurate acquisition of spatial distribution information for Apocynum venetum L. is highly important for the sustainable development of agriculture in Yuli County, Xinjiang. As an important cash crop, Apocynum relies on specific natural conditions for growth, and its survival environment is currently under severe threat. Therefore, accurately quantifying its spatial distribution information is crucial. This research takes Yuli County in Xinjiang as the study area and proposes an enhanced SegFormer model based on deep learning, aiming to realize the effective identification and extraction of Apocynum. The study indicates the following. (1) The improved SegFormer model adds smaller-scale feature layers in the encoder stage, allowing the improved model’s encoder to extract features at five scales: 1/4, 1/8, 1/16, 1/32, and 1/64; meanwhile, integrating the T2T-ViT backbone network into the encoder significantly enhances the precision and efficiency of Apocynum’s spatial distribution extraction. (2) Compared with Unet, TransUNet, and the original SegFormer, the improved SegFormer model outperforms the other models in terms of the mIoU, OA, and mPA metrics, achieving values of 88.22%, 93.98%, and 89.66%, respectively. (3) Ablation experiments show that the T2T_vit_14 model performs best among all the T2T-ViT configurations, with superior extraction effects on fragmented small plots compared with the other models. Therefore, the T2T_vit_14 model is integrated into the SegFormer model. This work improves the extraction accuracy and efficiency of the spatial distribution of Apocynum via an improved SegFormer model, which has strong stability and robustness and offers scientific evidence for resource protection, restoration planting, and germplasm breeding in Yuli County, Xinjiang.

Source: Remote Sensing

DOI: 10.3390/rs17173039

Authors: Yixuan Wang, Hong Wang, Xinhui Wang

Institutions: Beijing Normal University, Xinjiang University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,258