基于多尺度语义信息增强的农田地块提取网络

doi:10.16180/j.cnki.issn1007-7820.2023.07.010

Abstract

Abstract:

Facing the problems of adhesion of adjacent parcels and incomplete parcels due to the high heterogeneity and the small region among neighbor parcels, a farmland parcel extraction network based on multi-scale semantic information enhancement is proposed in this study. To alleviate the adhesion between parcels, the multi-scale feature extraction module with parallel structure is used, which retained the high-resolution feature maps to maintain high-precision edge information and reduce the loss of spatial location information due to downsampling. Furthermore, to reduce the phenomenon of incomplete parcels, the global semantic information enhancement module based on attentional mechanism is utilized to enhance the classification ability of the network by capture global semantic information instead of local semantic information. According to the experimental results, it is shown that the proposed method is 1%~13% better than the four typical algorithms in existing studies in terms of IoU, OA, and F1-score evaluation indexes.

Key words: high-resolution remote sensing imagery, farmland parcel, deep learning, semantic segmentation, multi-scale features, semantic information enhancement, adhesion of adjacent parcel, incomplete parcel

CLC Number:

TP751

ZENG Xinxin,ZHANG Hongyan. A Farmland Parcel Extraction Network Based on Multi-Scale Semantic Information Enhancement[J].Electronic Science and Technology, 2023, 36(7): 70-74.

Figures/Tables 5

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Table 1.

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References 20

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算法	IoU	OA	P	R	F1-score
SVM	0.6939	0.7603	0.8193	0.8883	0.7602
FCN-8s	0.7886	0.8601	0.9023	0.8593	0.8803
UNet	0.7835	0.8582	0.9125	0.8470	0.8785
DeeplabV3+	0.7982	0.8676	0.8622	0.9112	0.8860
Baseline	0.8054	0.8812	0.9423	0.8426	0.8897
本文方法	0.8165	0.8868	0.9414	0.8602	0.8990

A Farmland Parcel Extraction Network Based on Multi-Scale Semantic Information Enhancement

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